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References

Published online by Cambridge University Press:  10 August 2022

Vandana P. Janeja
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University of Maryland, Baltimore County
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Print publication year: 2022

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References

Abad, C., Taylor, J., Sengul, C., et al. (2003). “Log correlation for intrusion detection: a proof of concept.” 19th Annual Computer Security Applications Conference, 2003. Proceedings., 2003, pp. 255–264, doi: 10.1109/CSAC.2003.1254330.Google Scholar
Abdellahi, S., Lipford, H. R., Gates, C., and Fellows, J. (2020). “Developing a User Interface Security Assessment method.” USENIX Symposium on Usable Privacy and Security (SOUPS) 2020. August 9–11, 2020, Boston.Google Scholar
Abedin, M., Nessa, S., Khan, L., Al-Shaer, E., and Awad, M. (2010). “Analysis of firewall policy rules using traffic mining techniques.” International Journal of Internet Protocol Technology, 5(1–2), 322.CrossRefGoogle Scholar
Abraham, T. and Roddick, J. F. (1999). “Survey of spatio-temporal databases.” GeoInformatica, 3(1), 6199.CrossRefGoogle Scholar
Aggarwal, C. C. (2017). “Spatial outlier detection.” Outlier Analysis. Springer International Publishing, 345368.Google Scholar
Aggarwal, C. C. and Yu, P. S. (2001, May). “Outlier detection for high dimensional data.” ACM Sigmod Record, 30(2) 3746.Google Scholar
Agrawal, R., Gehrke, J., Gunopulos, D., and Raghavan, P. (1998). “Automatic subspace clustering of high dimensional data for data mining applications.” Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. ACM Press, 94105.Google Scholar
Agrawal, R. and Srikant, R. (1994). “Fast algorithms for mining association rules.” Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Bocca, J. B., Jarke, M., and Zaniolo, C. (Eds.). Morgan Kaufmann, 487499.Google Scholar
Agarwal, S., Farid, H., Gu, Y., et al. (2019, June). “Protecting world leaders against deep fakes.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 3845.Google Scholar
Ahmed, M., Mahmood, A. N., and Hu, J. (2016). “A survey of network anomaly detection techniques.” Journal of Network and Computer Applications, 60, 1931.CrossRefGoogle Scholar
Akamai. (2016). “Q4 2016 state of the internet/security report.” www.akamai.com/newsroom/press-release/akamai-releases-fourth-quarter-2016-state-of-the-internet-connectivity-report. Last accessed November 2021.Google Scholar
Akoglu, L., Tong, H., and Koutra, D. (2015). “Graph based anomaly detection and description: a survey.” Data Mining and Knowledge Discovery, 29(3), 626688.Google Scholar
Al-Musawi, B., Branch, P., and Armitage, G. (2015, December). “Detecting BGP instability using Recurrence Quantification Analysis (RQA).” Computing and Communications Conference (IPCCC), 2015 IEEE 34th International Performance. IEEE, 18.Google Scholar
Al-Musawi, B., Branch, P., and Armitage, G. (2016). “BGP anomaly detection techniques: a survey.” IEEE Communications Surveys & Tutorials, 19(1) 377396.Google Scholar
Al-Rousan, N. M. and Trajković, L. (2012, June). “Machine learning models for classification of BGP anomalies.2012 IEEE 13th International Conference on High Performance Switching and Routing (HPSR). IEEEV, 103108.Google Scholar
Al Shalabi, L., Shaaban, Z., and Kasasbeh, B. (2006). “Data mining: a preprocessing engine.” Journal of Computer Science, 2(9), 735739.Google Scholar
Aleroud, A. and Karabatis, G. (2013). “A system for cyber attack detection using contextual semantics.” 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing, Uden, L, Herrera, F, Pérez, J. B, and Corchado Rodríguez, J. M (Eds.). Springer, 431442.Google Scholar
Aleroud, A. and Karabatis, G. (2014, September). “Detecting zero-day attacks using contextual relations.” International Conference on Knowledge Management in Organizations, L. Uden, , D. Fuenzaliza Oshee, , I. H. Ting, , and D. Liberona, (Eds.). Springer, 373385.Google Scholar
Alperovitch, D. (2011). “Revealed: Operation Shady RAT.” www.csri.info/wp-content/uploads/2012/08/wp-operation-shady-rat1.pdf. Last accessed November 2021.Google Scholar
Alseadoon, I., Chan, T., Foo, E., and Gonzales, N. J. (2012). “Who is more susceptible to phishing emails? A Saudi Arabian study.” Proceedings of the 23rd Australasian Conference on Information Systems. ACIS, 111Google Scholar
Ameen, J. and Basha, R. “Mining time series for identifying unusual sub-sequences with applications.” ICICIC’06: Proceedings of the First International Conference on Innovative Computing, Information and Control. IEEE Computer Society, 574577.Google Scholar
Aminikhanghahi, S. and Cook, D. J. (2017). A survey of methods for time series change point detection. Knowledge and Information Systems, 51(2), 339367.Google Scholar
Anderson, L. C. and Agarwal, R. (2010, September). “Practicing safe computing: a multimethod empirical examination of home computer user security behavioral intentions.” MIS Quarterly, 34(3), 613643.CrossRefGoogle Scholar
Ankerst, M., Breunig, M. M., Kriegel, H. P., and Sander, J. (1999, June). “OPTICS: ordering points to identify the clustering structure.” Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data (SIGMOD '99). ACM, 4960. doi: 10.1145/304182.304187.Google Scholar
Azari, A., Janeja, V. P., and Levin, S. (2017). “MILES: multiclass imbalanced learning in ensembles through selective sampling.ACM Symposium on Applied Computing, Data Mining. ACM, 811816. doi: 10.1145/3019612.3019667.Google Scholar
Azari, A., Namayanja, J. M., Kaur, N., Misal, V., and Shukla, S. (2020, May). “Imbalanced learning in massive phishing datasets.2020 IEEE 6th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS). IEEE, 127132.Google Scholar
Banerjee, A., Venkatasubramanian, K. K., Mukherjee, T., and Gupta, S. K. S. (2012). “Ensuring safety, security, and sustainability of mission-critical cyber–physical systems.” Proceedings of the IEEE, 100(1), 283299.Google Scholar
Barnes, R. (2013, August 8). “Geocoding router log data.” http://resources.infosecinstitute.com/geocoding-router-log-data/#gref. Last accessed November 2021.Google Scholar
Barnett, V. and Lewis, R. (1994). Outliers in Statistical Data. John Wiley and Sons.Google Scholar
Basawa, I. V., Billard, L., and Srinivasan, R. (1984). “Large-sample tests of homogeneity for time series models.” Biometrika, 71(1), 203206.Google Scholar
Bellman, R. (1961). Adaptive Control Processes: A Guided Tour. Princeton University Press.Google Scholar
Ben-David, S., Borodin, A., Karp, R., Tardos, G., and Wigderson, A. (1994). “On the power of randomization in on-line algorithms.” Algorithmica, 11(1), 214.Google Scholar
Ben Salem, M., Hershkop, S., and Stolfo, S. J. (2008). “A survey of insider attack detection research.” Insider Attack and Cyber Security: Advances in Information Security, vol. 39, Stolfo, S. J, Bellovin, S. M, Keromytis, A. D, Hershkop, S, Smith, S. W, and Sinclair, S (Eds.). Springer, 119.Google Scholar
Ben Salem, M. and Stolfo, S. J. (2011). “Modeling user search behavior for masquerade detection.” Insider Attack and Cyber Security: Advances in Information Security, vol. 39, Stolfo, S. J, Bellovin, S. M, Keromytis, A. D, Hershkop, S, Smith, S. W, and Sinclair, S (Eds.). Springer. doi: 10.1007/978-0-387-77322-3_5.Google Scholar
Besag, J. and Newell, J. (1991).“The detection of clusters in rare diseases.” Journal of the Royal Statistical Society Series A, 154, 143155.Google Scholar
Beyer, M. A. and Laney, D. (2012). The Importance of “Big Data”: A Definition. Gartner.Google Scholar
Bhuyan, M. H., Bhattacharyya, D. K., and Kalita, J. K. (2014). “Network anomaly detection: methods, systems and tools.” IEEE Communications Surveys and Tutorials, 16(1), 303336.CrossRefGoogle Scholar
Binde, B. E., McRee, R., and O’Connor, T. (2011, May). “Assessing outbound traffic to uncover advanced persistent threat.” doi: 10.13140/RG.2.2.16401.07520.Google Scholar
Blasch, E., Kadar, I., Grewe, L. L., et al. (2017, May). “Panel summary of cyber-physical systems (CPS) and internet of things (IoT) opportunities with information fusion.” Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, vol. 10200, International Society for Optics and Photonics, 02000O.Google Scholar
Blasco, J. (2013, March 21). “New Sykipot developments.” https://cybersecurity.att.com/blogs/labs-research/new-sykipot-developments. Last accessed November 2021.Google Scholar
Brdiczka, O., Liu, J., Price, B., et al. (2012). “Proactive insider threat detection through graph learning and psychological context.” 2012 IEEE Symposium on Security and Privacy Workshops (SPW). IEEE, 142149.Google Scholar
Breiman, L. (1996). “Bagging predictors.” Machine Learning, 24(2), 123140.Google Scholar
Breiman, L. (2001). “Random forests.Machine Learning, 45(1), 532.Google Scholar
Breunig, M. M., Kriegel, H. P., Ng, R. T., and Sander, J. (1999, September). “Optics-of: identifying local outliers.” Principles of Data Mining and Knowledge Discovery. PKDD 1999, Żytkow, J. M and Rauch, J (Eds.) Lecture Notes in Computer Science, vol. 1704. Springer. doi: 10.1007/978-3-540-48247-5_28.Google Scholar
Brigham, E. O. (2002). The Fast Fourier Transform. Prentice Hall.Google Scholar
Bright, A. (2007). “Estonia accuses Russia of ‘cyberattack’.” www.csmonitor.com/2007/0517/p99s01-duts.html. Last accessed March 2020.Google Scholar
Bronskill, J. (2012, November 9). “Govt fears Canada becoming host country for cyber-attacker.” https://winnipeg.ctvnews.ca/canada-becoming-host-country-for-cyber-attackers-government-fears-.1032064. Last accessed November 2021.Google Scholar
Burns, C. (2012, June 1). “Stuxnet virus origin confirmed: USA and Isreali governments.” www.slashgear.com/stuxnet-virus-origin-confirmed-usa-and-isreali-governments-01231244/. Last accessed November 2021.Google Scholar
Caballero, J., Grier, C., Kreibich, C., and Paxson, V. (2011, August). “Measuring pay-per-install: the commoditization of malware distribution.” Proceedings of the 20th USENIX Conference on Security (SEC'11), 13.Google Scholar
Cai, L. and Hao, C. (2011). “TouchLogger: inferring keystrokes on touch screen from smartphone motion.” HotSec, 11(2011), 99.Google Scholar
Caldwell, D., Gilbert, A., Gottlieb, J., et al. (2004). “The cutting EDGE of IP router configuration.” ACM SIGCOMM Computer Communication Review, 34(1), 2126.CrossRefGoogle Scholar
Calvaresi, D., Marinoni, M., Sturm, A., Schumacher, M., and Buttazzo, G. (2017, August). “The challenge of real-time multi-agent systems for enabling IoT and CPS.Proceedings of the International Conference on Web Intelligence. ACM, 356364.Google Scholar
Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J., and Rundensteiner, E. A. (2014, March). “Scalable distance-based outlier detection over high-volume data streams.2014 IEEE 30th International Conference on Data Engineering (ICDE) . IEEE, 7687.CrossRefGoogle Scholar
Carvalho, M., DeMott, J., Ford, R., and Wheeler, D. A. (2014). “Heartbleed 101.” IEEE Security & Privacy, 12(4), 6367.Google Scholar
Chandola, V., Banerjee, A., and Kumar, V. (2009). “Anomaly detection: a survey.” ACM Computing Surveys (CSUR), 41(3), 15.Google Scholar
Chandrashekar, G. and Sahin, F. (2014). “A survey on feature selection methods.” Computers & Electrical Engineering, 40(1), 1628.Google Scholar
Chapman, P., Clinton, J., Kerber, R., et al. (2000). CRISP-DM 1.0, Step-by-Step Data Mining Guide. CRISP-DM Consortium; SPSS: Chicago, IL, USA.Google Scholar
Check Point. (2020). “Threat map.” https://threatmap.checkpoint.com/. Last accessed March 2020.Google Scholar
Chen, M., Mao, S., and Liu, Y. (2014). “Big data: a survey.” Mobile Networks and Applications 19(2), 171209.Google Scholar
Chen, S. and Janeja, V. P. (2014). “Human perspective to anomaly detection for cybersecurity.” Journal of Intelligent Information Systems, 42(1), 133153.Google Scholar
Cheok, R. (2014). “Wire shark: a guide to color my packets detecting network reconnaissance to host exploitation.” GIAC Certification Paper. SANS Institute Reading Room.Google Scholar
Cheswick, B. (1992, January). “An evening with Berferd in which a cracker is lured, endured, and studied.” Proceedings of the Winter USENIX Conference, San Francisco, 2024.Google Scholar
Chi, M. (2014). “Cyberspace: America’s new battleground.” www.sans.org/reading-room/whitepapers/warfare/cyberspace-americas-battleground-35612. Last accessed November 2021.Google Scholar
Chien, E. and O’Gorman, G. (2011). “The nitro attacks, stealing secrets from the chemical industry.” Symantec Security Response (2011), 18.Google Scholar
CIA. (2021). “World Factbook.” Last accessed November 2021.Google Scholar
Cleary, J. G. and Trigg, L. E. (1995). “K*: an instance-based learner using an entropic distance measure.” Machine Learning Proceedings 1995, Prieditis, A and Russell, S (Eds.). Morgan Kaufmann, 108114.CrossRefGoogle Scholar
Cloud Security Alliance. (2014). Big data taxonomy. https://downloads.cloudsecurityalliance.org/initiatives/bdwg/Big_Data_Taxonomy.pdf. Last accessed April 13, 2017.Google Scholar
Cohen, E., Datar, M., Fujiwara, S., et al. (2001). “Finding interesting associations without support pruning.” IEEE Transactions on Knowledge and Data Engineering, 13(1), 6478.CrossRefGoogle Scholar
Cohen, W. W. (1995, July). “Fast effective rule induction.” Proceedings of the Twelfth International Conference on Machine Learning. Morgan Kaufmann, 115123.Google Scholar
Cooper, G. F. and Herskovits, E. (1992). “A Bayesian method for the induction of probabilistic networks from data.” Machine Learning, 9, 309347.Google Scholar
Cortes, C. and Vapnik, V. (1995). “Support-vector networks.” Machine Learning, 20(3), 273297.CrossRefGoogle Scholar
Cover, T. and Hart, P. (1967). “Nearest neighbor pattern classification.” IEEE Transactions on Information Theory, 13(1), 2127.Google Scholar
Dark Reading. (2011). “PNNL attack: 7 lessons: surviving a zero-day attack.” www.darkreading.com/attacks-and-breaches/7-lessons-surviving-a-zero-day-attack/d/d-id/1100226. Last accessed March 2020.Google Scholar
Darwish, A. and Bataineh, E. (2012, December 18–20). “Eye tracking analysis of browser security indicators.” 2012 International Conference on Computer Systems and Industrial Informatics (ICCSII), 1, 6. doi: 10.1109/ICCSII.2012.6454330. Last accessed November 2021.Google Scholar
Das, A., Ng, W.-K., and Woon, Y.-K. 2001. “Rapid association rule mining.” Proceedings of the Tenth International Conference on Information and Knowledge Management. ACM Press, 474481.Google Scholar
Das, S., Kim, A., Jelen, B., Huber, L., and Camp, L. J. (2020). “Non-inclusive online security: older adults’ experience with two-factor authentication.” Proceedings of the 54th Hawaii International Conference on System Sciences, 6472.Google Scholar
Dash, M. and Liu, H. (1997). “Feature selection for classification.” Intelligent Data Analysis, 1(1–4), 131156.Google Scholar
Davies, C. “Flame cyber-espionage discovered in vast infection net.” (2012, May 28). www.slashgear.com/flame-cyber-espionage-discovered-in-vast-infection-net-28230470/. Last accessed November 2021.Google Scholar
Davinson, N. and Sillence, E. (2010). “It won’t happen to me: Promoting secure behaviour among internet users.” Computers in Human Behavior, 26(6), 17391747.Google Scholar
Davinson, N. and Sillence, E. (2014). “Using the health belief model to explore users’ perceptions of ‘being safe and secure’ in the world of technology mediated financial transactions.International Journal of Human–Computer Studies, 72(2), 154168.Google Scholar
Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). “Maximum likelihood from incomplete data via the EM algorithm.” Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 122.Google Scholar
Deokar, B. and Hazarnis, A. (2012). “Intrusion detection system using log files and reinforcement learning.International Journal of Computer Applications 45(19), 2835.Google Scholar
Dey, S., Janeja, V. P., and Gangopadhyay, A. (2009). “Temporal neighborhood discovery through unequal depth binning.” IEEE International Conference on Data Mining (ICDM’09), 110119.Google Scholar
Dey, S., Janeja, V. P., and Gangopadhyay, A. (2014). “Discovery of temporal neighborhoods through discretization methods.” Intelligent Data Analysis, 18(4), 609636.Google Scholar
Ding, Y., Yan, E., Frazho, A., and Caverlee, J. (2009, November). “Pagerank for ranking authors in co-citation networks.” Journal of the American Society for Information Science and Technology, 60(11), 22292243.Google Scholar
Domo. (2017). “Data never sleeps.” www.domo.com/learn/data-never-sleeps-5?aid=ogsm072517_1&sf100871281=1. Last accessed November 2021.Google Scholar
Drinkwater, D. (2016). “Does a data breach really affect your firm’s reputation?” www.csoonline.com/article/3019283/data-breach/does-a-data-breach-really-affect-your-firm-s-reputation.html. Last accessed June 2017.Google Scholar
Duchene, F., Garbayl, C., and Rialle, V. (2004). “Mining heterogeneous multivariate time-series for learning meaningful patterns: application to home health telecare.” arXiv preprint cs/0412003.Google Scholar
Duczmal, L. and Renato, A. (2004). “A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters.Computational Statistics and Data Analysis, 45(2), 269286.Google Scholar
Eberle, W., Graves, J., and Holder, L. (2010). “Insider threat detection using a graph-based approach.” Journal of Applied Security Research, 6(1), 3281.Google Scholar
ENISA (European Union Agency for Network And Information Security). (2016, Jan). “Threat Taxonomy: a tool for structuring threat information.” https://library.cyentia.com/report/report_001462.html. Last accessed November 2021.Google Scholar
Ester, M., Frommelt, A., Kriegel, H.-P., and Sander, J. (1998). “Algorithms for characterization and trend detection in spatial databases.” Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD'98), 4450.Google Scholar
Ester, M., Kriegel, H., and Sander, J. (1997). “Spatial data mining: a database approach.” The 5th International Symposium on Advances in Spatial Databases, Springer-Verlag, 4766.Google Scholar
Ester, M., Kriegel, H. P., Sander, J., and Xu, X. (1996, August). “A density-based algorithm for discovering clusters in large spatial databases with noise.” KDD, 96(34), 226231.Google Scholar
Estevez-Tapiador, J. M., Garcia-Teodoro, P., and Diaz-Verdejo, J. E. (2004). “Anomaly detection methods in wired networks: a survey and taxonomy.” Computer Communications, 27(16), 15691584.Google Scholar
Fabrikant, A., Koutsoupias, E., and Papadimitriou, C. H. (2002). “Heuristically optimized trade-offs: a new paradigm for power laws in the Internet.” International Colloquium on Automata, Languages and Programming. Springer, 110122.Google Scholar
Faloutsos, M., Faloutsos, P., and Faloutsos, C. (1999). “On power-law relationships of the internet topology.” The Structure and Dynamics of Networks, Newman, M, Barabási, A.-L, and Watts, D. J. (Eds.). Princeton University Press, 195206.Google Scholar
Famili, A., Shen, W. M., Weber, R., and Simoudis, E. (1997). “Data preprocessing and intelligent data analysis.” Intelligent Data Analysis, 1(1–4), 323.Google Scholar
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). “From data mining to knowledge discovery in databases.” AI Magazine 17(3), 37.Google Scholar
Feily, M., Shahrestani, A., and Ramadass, S. (2009, June). “A survey of botnet and botnet detection.” Third International Conference on Emerging Security Information, Systems and Technologies, 2009. SECURWARE’09. IEEE, 268273.Google Scholar
Ferebee, D., Dasgupta, D., and Wu, Q. (2012, December). “A cyber-security storm map.” 2012 International Conference on Cyber Security (CyberSecurity). IEEE, 93102.Google Scholar
FireEye. (2019). “Mandiant Purple Team Assessment data sheet.” www.fireeye.com/content/dam/fireeye-www/services/pdfs/pf/ms/ds-purple-team-assessment.pdf. Last accessed November 2021.Google Scholar
Fischer, P., Lea, S. E., and Evans, K. M. (2013). “Why do individuals respond to fraudulent scam communications and lose money? The psychological determinants of scam compliance.” Journal of Applied Social Psychology, 43(10), 20602072.Google Scholar
Fodor, I. K. (2002). “A survey of dimension reduction techniques.” Center for Applied Scientific Computing, Lawrence Livermore National Laboratory 9, 118.Google Scholar
Frank, E., and Witten, H. I. (1998). “Generating accurate rule sets without global optimization.Proceedings of the 15th International Conference on Machine Learning (ICML’98), Madison, Wisconsin, Frank, E and Witten, I. H. (Eds.). Morgan Kaufmann, 144151.Google Scholar
Frank, L., Greitzer, R., and Hohimer, E. (2011, Summer). “Modeling human behavior to anticipate insider attacks.” Journal of Strategic Security: Strategic Security in Cyber Age, 4(2), 2548.Google Scholar
Freeman, L. C. (1978). “Centrality in social networks conceptual clarification.” Social Networks, 1(3), 215239.Google Scholar
Frei, S., May, M., Fiedler, U., and Plattner, B. (2006, September). “Large-scale vulnerability analysis.Proceedings of the 2006 SIGCOMM Workshop on Large-Scale Attack Defense. ACM, 131138.Google Scholar
Freund, Y. and Schapire, R. E. (1996). “Experiments with a new boosting algorithm.Proceedings of the 13th International Conference on Machine Learning. Morgan Kaufmann, 148146.Google Scholar
Gandhi, R., Sharma, A., Mahoney, W., et al. (2011). “Dimensions of cyber-attacks: cultural, social, economic, and political.” IEEE Technology and Society Magazine, 30(1), 2838.Google Scholar
Garcia, S., Luengo, J., Sáez, J. A., Lopez, V., and Herrera, F. (2013). “A survey of discretization techniques: taxonomy and empirical analysis in supervised learning.” IEEE Transactions on Knowledge and Data Engineering, 25(4), 734750.Google Scholar
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., and Vázquez, E. (2009). “Anomaly-based network intrusion detection: techniques, systems and challenges.” Computers and Security, 28(1), 1828.Google Scholar
Garg, A., Upadhyaya, S., and Kwiat, K. (2013). “A user behavior monitoring and profiling scheme for masquerade detection.” Handbook of Statistics: Machine Learning: Theory and Applications, 31, 353.Google Scholar
Geenens, P. (2020). “FireEye hack turns into a global supply chain attack.” https://securityboulevard.com/2020/12/fireeye-hack-turns-into-a-global-supply-chain-attack/. Last accessed November 2021.Google Scholar
Gennari, J. H., Langley, P., and Fisher, D. (1989). “Models of incremental concept formation.” Artificial Intelligence, 40(1–3), 1161.Google Scholar
Gesenhues, A. (2014). “Google Dorking: it’s all fun & games until the hackers show up.” http://searchengineland.com/google-dorking-fun-games-hackers-show-202191. Last accessed November 2021.Google Scholar
Glaz, J., Naus, J., and Wallenstein, S. (2001). Scan Statistics. Springer Verlag.Google Scholar
Goldberg, L. R. (1990). “An alternative ‘description of personality’: the big-five factor structure.” Journal of Personality and Social Psychology, 59(6), 1216.Google Scholar
Golnabi, K., Min, R. K,. Khan, L., and Al-Shaer, E. (2006). “Analysis of firewall policy rules using data mining techniques.” 2006 IEEE/IFIP Network Operations and Management Symposium, NOMS 2006. IEEE/IFIP, 205315.Google Scholar
Goodfellow, I. (2016). “NIPS 2016 tutorial: generative adversarial networks.” arXiv:1701.00160.Google Scholar
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). “Generative adversarial nets.” Proceedings of the 27th International Conference on Neural Information Processing Systems – Volume 2 (NIPS'14). MIT Press, 26722680.Google Scholar
Gopalani, S. and Arora, R. (2015). “Comparing Apache Spark and Map Reduce with performance analysis using K-means.” International Journal of Computer Applications, 113(1), 811.Google Scholar
Gormley, T., Reingold, N., Torng, E., and Westbrook, J. (2000). “Generating adversaries for request-answer games.Proceedings of the 11th ACM-SIAM Symposium on Discrete Algorithms. ACM-SIAM, 564565.Google Scholar
Grazioli, S. (2004). “Where did they go wrong? An analysis of the failure of knowledgeable internet consumers to detect deception over the internet.” Group Decision and Negotiation, 13(2), 149172.Google Scholar
Griffith, D. A. (1987). Spatial Autocorrelation: A Primer. Association of American Geographers.Google Scholar
Grover, A., Gholap, J Janeja, V. P, et al. (2015). “SQL-like big data environments: case study in clinical trial analytics.” Proceedings of 2015 IEEE International Conference on Big Data, 26802689.Google Scholar
Gu, G., Zhang, J., and Lee, W. (2008, February). “BotSniffer: detecting botnet command and control channels in network traffic.” Proceedings of the 15th Annual Network and Distributed System Security Symposium, vol. 8, 118.Google Scholar
Guardian, The. (2016). “Norway, the country where you can see everyone’s tax returns.” www.theguardian.com/money/blog/2016/apr/11/when-it-comes-to-tax-transparency-norway-leads-the-field. Last accessed November 2021.Google Scholar
Guha, S., Rastogi, R., and Shim, K. (1998, June). “CURE: an efficient clustering algorithm for large databases.” ACM Sigmod Record, 27(2), 7384.Google Scholar
Guha, S., Rastogi, R., and Shim, K. (2000). “ROCK: a robust clustering algorithm for categorical attributes.” Information Systems, 25(5), 345366.Google Scholar
Gupta, H., Sural, S., Atluri, V., and Vaidya, J. (2016). “Deciphering text from touchscreen key taps.” IFIP Annual Conference on Data and Applications Security and Privacy. Springer International Publishing, 318.Google Scholar
Guralnik, V. and Srivastava, J. (1999). “Event detection from time series data.” KDD’99: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 3342.Google Scholar
Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge University Press.Google Scholar
Halevi, T., Lewis, J., and Memon, N. (2013, May). “A pilot study of cyber security and privacy related behavior and personality traits.Proceedings of the 22nd International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 737744.Google Scholar
Halliday, J. (2010, September 24). “Stuxnet worm is the ‘work of a national government agency’.” www.guardian.co.uk/technology/2010/sep/24/stuxnet-worm-national-agency. Last accessed November 2021.Google Scholar
Han, J., and Fu, Y. (1995, September). “Discovery of multiple-level association rules from large databases.VLDB’95, Proceedings of 21th International Conference on Very Large Data Bases, Dayal, U, Gray, P, and Nishio, S (Eds.). Morgan Kaufmann, 420431.Google Scholar
Han, J., Pei, J., and Yin, Y. (2000, May). “Mining frequent patterns without candidate generation.” ACM Sigmod Record, 29(2), 112.Google Scholar
Hellerstein, J. L., Ma, S., and Perng, C.-S. (2002). “Discovering actionable patterns in event data.” IBM Systems Journal,  41(3), 475493.Google Scholar
Heron, S. (2007). “The rise and rise of the keyloggers.” Network Security 2007(6), 46.Google Scholar
Hinneburg, A., and Keim, D. A. (1998, August). “An efficient approach to clustering in large multimedia databases with noise.” KDD, 98, 5865.Google Scholar
Hoffman, S. (2011). “Cyber attack forces internet shut down for DOE lab, on July 8.” www.crn.com/news/security/231001261/cyber-attack-forces-internet-shut-down-for-doe-lab.htm. Last accessed February 23, 2014.Google Scholar
Hong, J., Liu, C. C., and Govindarasu, M. (2014). “Integrated anomaly detection for cyber security of the substations.” IEEE Transactions on Smart Grid, 5(4), 16431653.Google Scholar
Hu, M. and Liu, B. (2004, August). “Mining and summarizing customer reviews.” Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 168177.Google Scholar
Hu, Z., Baynard, C. W., Hu, H., and Fazio, M. (2015, June). “GIS mapping and spatial analysis of cybersecurity attacks on a Florida university.” 2015 23rd International Conference on Geoinformatics. IEEE, 15.Google Scholar
Hu, Z., Wang, H., Zhu, J., et al. (2014). “Discovery of rare sequential topic patterns in document stream.” Proceedings of the 2014 SIAM International Conference on Data Mining, 533541.Google Scholar
Huang, Y., Pei, J., and Xiong, H. (2006). “Mining co-location patterns with rare events from spatial data sets.” GeoInformatica, 10(3), 239260.Google Scholar
Hussain, M., Al-Haiqi, A., Zaidan, A. A., Zaidan, B. B., Kiah, M. M., Anuar, N. B., and Abdulnabi, M. (2016). “The rise of keyloggers on smartphones: A survey and insight into motion-based tap inference attacks.” Pervasive and Mobile Computing, 25, 125.Google Scholar
Ingols, K., Lippmann, R., & Piwowarski, K. (2006, December). “Practical attack graph generation for network defense.” Computer Security Applications Conference, 2006. ACSAC’06. 22nd Annual. IEEE, 121130.Google Scholar
Iyengar, V. S. (2004). “On detecting space-time clusters.” KDD’04: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 587592.Google Scholar
Jain, A. K. (2010). “Data clustering: 50 years beyond K-means.” Pattern Recognition Letters, 31(8), 651666.Google Scholar
Jakobsson, M. (2007). “The human factor in phishing.” Privacy & Security of Consumer Information, 7(1), 119.Google Scholar
Janeja, V. P. (2019). Do No Harm: An Ethical Data Life Cycle, Sci on the Fly. AAAS.Google Scholar
Janeja, V. P., Adam, N. R., Atluri, V., and Vaidya, J. (2010). “Spatial neighborhood based anomaly detection in sensor datasets.” Data Mining and Knowledge Discovery, 20(2), 221258.Google Scholar
Janeja, V. P. and Atluri, V. (2005). “LS3: A linear semantic scan statistic technique for detecting anomalous windows. Proceedings of the 2005 ACM Symposium on Applied Computing, 493497.Google Scholar
Janeja, V. P. and Atluri, V. (2005). “FS3: A random walk based free-form spatial scan statistic for anomalous window detection.Fifth IEEE International Conference on Data Mining (ICDM’05). IEEE Computer Society, 661664.Google Scholar
Janeja, V. P, and Atluri, V. (2008). “Random walks to identify anomalous free-form spatial scan windows.” IEEE Transactions on Knowledge and Data Engineering, 20(10), 13781392.Google Scholar
Janeja, V. P. and Atluri, V. (2009). “Spatial outlier detection in heterogeneous neighborhoods.” Intelligent Data Analysis, 13(1), 85107.Google Scholar
Janeja, V. P., Azari, A., Namayanja, J. M., and Heilig, B. (2014, October). “B-dids: Mining anomalies in a Big-distributed Intrusion Detection System.” In 2014 IEEE International Conference on Big Data (Big Data) (pp. 32–34). IEEE.Google Scholar
Jarvis, R. A. and Patrick, E. A. (1973). “Clustering using a similarity measure based on shared near neighbors.” IEEE Transactions on Computers, 100(11), 10251034.Google Scholar
Jha, S., Sheyner, O., and Wing, J. (2002). “Two formal analyses of attack graphs.” Computer Security Foundations Workshop, 2002. Proceedings. 15th IEEE. IEEE, 4963.Google Scholar
Ji, Y., Zhang, X., Ji, S., Luo, X., and Wang, T. (2018, October). “Model-reuse attacks on deep learning systems.” Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. ACM, 349363.Google Scholar
Joachims, T. (2002, July). “Optimizing search engines using clickthrough data.” Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 133142.Google Scholar
John, O. P. and Srivastava, S. (1999). “The big-five trait taxonomy: history, measurement, and theoretical perspectives.” Handbook of Personality: Theory and Research, vol. 2., Pervin, L. A. and John, O. P. (Eds.). Guilford Press, 102138.Google Scholar
Kang, I., Kim, T., and Li, K. (1997). “A spatial data mining method by Delaunay triangulation.” Proceedings of the 5th ACM International Workshop on Advances in Geographic Information Systems. ACM, 3539.Google Scholar
Kang, U., Tsourakakis, C., Appel, A., Faloutsos, C., and Leskovec, J. (2010). “Radius plots for mining tera-byte scale graphs: algorithms, patterns, and observations.” Proceedings of the 2010 SIAM International Conference on Data Mining (SDM), 548558.Google Scholar
Kang, U., Tsourakakis, C., and Faloutsos, C. (2009). “Pegasus: a peta-scale graph mining system – implementation and observations.” 2009 Ninth IEEE International Conference on Data Mining, 229238.Google Scholar
Karypis, G., Han, E. H., and Kumar, V. (1999). “Chameleon: hierarchical clustering using dynamic modeling.” Computer, 32(8), 6875.Google Scholar
Kaspersky. (2020). “Cyberthreat real-time map.” https://cybermap.kaspersky.com/. Last accessed November 2020.Google Scholar
Kath, O., Schreiner, R., and Favaro, J. (2009, September). “Safety, security, and software reuse: a model-based approach.” Proceedings of the Fourth International Workshop in Software Reuse and Safety. www.researchgate.net/publication/228709911_Safety_Security_and_Software_Reuse_A_Model-Based_Approach. Last accessed November 2021.Google Scholar
Kato, K. and Klyuev, V. (2017, August). “Development of a network intrusion detection system using Apache Hadoop and Spark.2017 IEEE Conference on Dependable and Secure Computing. IEEE, 416423.Google Scholar
Katsini, C., Abdrabou, Y., Raptis, G. E., Khamis, M., and Alt, F. (2020, April). “The role of eye gaze in security and privacy applications: survey and future HCI research directions.” Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 121.CrossRefGoogle Scholar
Kaufman, L. and Rousseeuw, P. (1987). Clustering by Means of Medoids. North-Holland.Google Scholar
Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons.Google Scholar
Keim, D. A., Mansmann, F., Panse, C., Schneidewind, J., and Sips, M. (2005). “Mail explorer – spatial and temporal exploration of electronic mail.” Proceedings of the Seventh Joint Eurographics/IEEE VGTC Conference on Visualization, 247254.Google Scholar
Keim, D. A., Mansmann, F., and Schreck, T. (2005). “Analyzing electronic mail using temporal, spatial, and content-based visualization techniques.” Informatik 2005–Informatik Live!, vol. 67, 434438.Google Scholar
Keogh, E., Lin, J., and Fu, A. (2005). “Hot sax: efficiently finding the most unusual time series subsequence.” Fifth IEEE International Conference on Data Mining (ICDM'05), doi: 10.1109/ICDM.2005.79.Google Scholar
Kianmehr, K. and Koochakzadeh, N. (2012). “Learning from socio-economic characteristics of IP geo-locations for cybercrime prediction.” International Journal of Business Intelligence and Data Mining, 7(1/2), 2139Google Scholar
Kim, S., Edmonds, W., and Nwanze, N. 2014. “On GPU accelerated tuning for a payload anomaly-based network intrusion detection scheme.Proceedings of the 9th Annual Cyber and Information Security Research Conference (CISR ‘14). ACM, 14. doi: 10.1145/2602087.2602093.Google Scholar
Kim Zetter Security. (2013). “Someone’s been siphoning data through a huge security hole in the internet.” www.wired.com/2013/12/bgp-hijacking-belarus-iceland/. Last accessed December 2016.Google Scholar
Knorr, E. M., Ng, R. T., and Tucakov, V. (2000). “Distance-based outliers: algorithms and applications.” VLDB Journal – The International Journal on Very Large Data Bases, 8(3–4), 237253.Google Scholar
Koh, Y. S. and Ravana, S. D. (2016). “Unsupervised rare pattern mining: a survey.” ACM Transactions on Knowledge Discovery from Data (TKDD), 10(4), 45.Google Scholar
Koh, Y. S. and Rountree, N. (2005). “Finding sporadic rules using apriori-inverse.” PAKDD (Lecture Notes in Computer Science), vol. 3518. Ho, T. B., Cheung, D., and Liu, H. (Eds.). Springer, 97106.Google Scholar
Koike, H. and Ohno, K. (2004). “SnortView: visualization system of snort logs.” Proceedings of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security (VizSEC/DMSEC '04). ACM, 143147. doi: 10.1145/1029208.1029232.Google Scholar
Kosner, A. W. (2012). “Cyber security fails as 3.6 million social security numbers breached in South Carolina.” www.forbes.com/sites/anthonykosner/2012/10/27/cyber-security-fails-as-3-6-million-social-security-numbers-breached-in-south-carolina/?sh=5f3637784e9e. Last accessed March 2021.Google Scholar
Kotsiantis, S. and Pintelas, P. (2004). “Recent advances in clustering: a brief survey.” WSEAS Transactions on Information Science and Applications, 1(1), 7381.Google Scholar
Kotsiantis, S. B., Zaharakis, I. D., and Pintelas, P. E. (2006). “Machine learning: a review of classification and combining techniques.” Artificial Intelligence Review, 26(3), 159190.Google Scholar
Kulldorff, M. (1997). “A spatial scan statistic.” Communications of Statistics – Theory Meth., 26(6), 14811496.Google Scholar
Kulldorff, M., Athas, W., Feuer, E., Miller, B., and Key, C. (1998). “Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos.” American Journal of Public Health, 88(9), 13771380.Google Scholar
Kurgan, L. A. and Musilek, P. (2006, March). “A survey of knowledge discovery and data mining process models.” Knowledge Engineering Review, 21(1) 124. doi: 10.1017/S0269888906000737.Google Scholar
L24. (2016). “First national cyber security exercise Cyber Shield.” http://l24.lt/en/society/item/150489-first-national-cyber-security-exercise-cyber-shield-2016-will-be-held. Last accessed April 12, 2017.Google Scholar
LeCun, Y., Bengio, Y., and Hinton, G. (2015). “Deep learning.” Nature, 521(7553), 436.Google Scholar
Lee, G., Yun, U., Ryang, H., and Kim, D. (2015). “Multiple minimum support-based rare graph pattern mining considering symmetry feature-based growth technique and the differing importance of graph elements.” Symmetry, 7(3), 1151.Google Scholar
Leskovec, J. (2008). “Dynamics of large networks.” Dissertation. ProQuest Dissertations Publishing.Google Scholar
Leskovec, J., Chakrabarti, D., Kleinberg, J., and Faloutsos, C. (2005). “Realistic, mathematically tractable graph generation and evolution, using Kronecker multiplication.” European Conference on Principles and Practice of Knowledge Discovery in Databases: PKDD 2005, Jorge, A. M, Torgo, L, Brazdil, P, Camacho, R, and Gama, J (Eds.). Lecture Notes in Computer Science, vol. 3721. Springer. doi: 10.1007/11564126_17.Google Scholar
Leskovec, J. and Faloutsos, C. (2007). “Scalable modeling of real graphs using kronecker multiplication.International Conference on Machine Learning (ICML ‘07). ACM, 497504. doi: 10.1145/1273496.1273559.Google Scholar
Leskovec, J., Kleinberg, J., and Faloutsos, C. (2005, August). “Graphs over time: densification laws, shrinking diameters and possible explanations.Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 177187.Google Scholar
Leskovec, J., Kleinberg, J., and Faloutsos, C. (2007). “Graph evolution: densification and shrinking diameters.ACM Transactions on Knowledge Discovery from Data (TKDD), 1, 2-es. doi: 10.1145/1217299.1217301.Google Scholar
Lewis, D. M. and Janeja, V. P. (2011). “An empirical evaluation of similarity coefficients for binary valued data.” International Journal of Data Warehousing and Mining (IJDWM), 7(2), 4466. doi: 10.4018/jdwm.2011040103.CrossRefGoogle Scholar
Lewis, J. A. (2005). “Computer espionage, Titan Rain and China.” http://csis.org/files/media/csis/pubs/051214_china_titan_rain.pdf. Last accessed March 2020.Google Scholar
Leyden, J. (2012, March 29). “NSA’s top spook blames China for RSA hack.” www.theregister.co.uk/2012/03/29/nsa_blames_china_rsa_hack/. Last accessed November 2021.Google Scholar
Li, J., Dou, D., Wu, Z., Kim, S., and Agarwal, V. (2005). “An Internet routing forensics framework for discovering rules of abnormal BGP events.” ACM SIGCOMM Computer Communication Review, 35(5), 5566.Google Scholar
Li, X., Wang, L., & Sung, E. (2008). AdaBoost with SVM-based component classifiers. Engineering Applications of Artificial Intelligence, 21(5), 785795.Google Scholar
Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). “A symbolic representation of time series, with implications for streaming algorithms.” DMKD’03: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. ACM, 211.Google Scholar
Lin, Q., Adepu, S., Verwer, S., and Mathur, A. (2018, May). “TABOR: a graphical model-based approach for anomaly detection in industrial control systems.” Proceedings of the 2018 on Asia Conference on Computer and Communications Security (ASIACCS). ACM, 525536. doi: 10.1145/3196494.3196546.Google Scholar
Liu, B., Hsu, W., and Ma, Y. (1999a). “Mining association rules with multiple minimum supports.Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 337341.Google Scholar
Liu, H., Hussain, F., Tan, C. L., and Dash, M. (2002). “Discretization: an enabling technique.” Data Mining and Knowledge Discovery, 6(4), 393423.Google Scholar
Liu, Z., Wang, C., and Chen, S. (2008). “Correlating multi-step attack and constructing attack scenarios based on attack pattern modeling.International Conference on Information Security and Assurance, 2008. ISA 2008. IEEE, 214219.Google Scholar
Limmer, T. and Dressler, F. (2010). “Dialog-based payload aggregation for intrusion detection.” Proceedings of the 17th ACM Conference on Computer and Communications Security (CCS ‘10). ACM, 708710. doi: 10.1145/1866307.1866405.Google Scholar
Lockheed Martin. (2015). “Gaining the advantage, applying Cyber Kill Chain® methodology to network defense 2015.” Technical report. www.lockheedmartin.com/content/dam/lockheed-martin/rms/documents/cyber/Gaining_the_Advantage_Cyber_Kill_Chain.pdf. Last accessed November 2021.Google Scholar
Luengo, J., García, S., and Herrera, F. (2012). “On the choice of the best imputation methods for missing values considering three groups of classification methods.” Knowledge and Information Systems, 32(1), 77108.Google Scholar
Ma, S. and Hellerstein, J. L. (2001a). “Mining mutually dependent patterns.Proceedings of the 2001 International Conference on Data Mining (ICDM’01), San Jose, CA, November 2001. IEEE, 409416.Google Scholar
Ma, S. and Hellerstein, J. L. (2001b). “Mining partially periodic event patterns with unknown periods.” Proceedings of the 2001 International Conference on Data Engineering (ICDE’01), Heidelberg, Germany, April 2001. IEEE, 205214.Google Scholar
MacQueen, J. (1967, June). “Some methods for classification and analysis of multivariate observations.Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, vol. 1, no. 14, 281297.Google Scholar
Mandiant. (2013). “APT1: exposing one of China’s cyber espionage units.” www.mandiant.com/resources/apt1-exposing-one-of-chinas-cyber-espionage-units. Last accessed November 2021.Google Scholar
Manyika, J., Chui, M., Brown, B., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.Google Scholar
Maron, M. and Kuhns, J. (1960). “On relevance, probabilistic indexing, and information retrieval.” Journal of the Association for Computing Machinery 7, 216244.Google Scholar
Massicotte, F., Whalen, T., and Bilodeau, C. (2003). “Network mapping tool for real-time security analysis.” RTO IST Symposium on Real Time Intrusion Detection, 12-1–12-10.Google Scholar
McAfee. (2010). “Protecting your critical assets.” www.wired.com/images_blogs/threatlevel/2010/03/operationaurora_wp_0310_fnl.pdf. Last accessed November 2021.Google Scholar
McAfee. (2011). “Global energy cyberattacks: “Night Dragon.” www.heartland.org/publications-resources/publications/global-energy-cyberattacks-night-dragon. Last accessed November 2021.Google Scholar
McAfee. (2018). “The economic impact of cybercrime – no slowing down.” McAfee, Center for Strategic and International Studies (CSIS). www.mcafee.com/enterprise/en-us/solutions/lp/economics-cybercrime.html.Google Scholar
McBride, M., Carter, L., and Warkentin, M. (2012). “Exploring the role of individual employee characteristics and personality on employee compliance with cybersecurity policies.” RTI International Institute for Homeland Security Solutions, 5(1), 1.Google Scholar
McGuire, M. P., Janeja, V.P., and Gangopadhyay, A. (2008, August). “Spatiotemporal neighborhood discovery for sensor data.” International Workshop on Knowledge Discovery from Sensor Data. Springer, 203225.Google Scholar
McGuire, M. P., Janeja, V. P., and Gangopadhyay, A. (2012). “Mining sensor datasets with spatio-temporal neighborhoods.Journal of Spatial Information Science (JOSIS), 2013(6), 142.Google Scholar
Meng, X., Bradley, J., Yavuz, B., et al. (2016). “Mllib: machine learning in Apache Spark.” Journal of Machine Learning Research, 17(1), 12351241.Google Scholar
Mezzour, G. (2015). “Assessing the global cyber and biological threat.” Thesis, Carnegie Mellon University. doi: 10.1184/R1/6714857.v1.Google Scholar
Miller, H. J. (2004). “Tobler’s first law and spatial analysis.” Annals of the Association of American Geographers, 94(2), 284289.Google Scholar
Miller, W. B. (2014). “Classifying and cataloging cyber-security incidents within cyber-physical systems.” Doctoral dissertation, Brigham Young University.Google Scholar
Misal, V., Janeja, V. P., Pallaprolu, S. C., Yesha, Y., and Chintalapati, R. (2016, December). “Iterative unified clustering in big data.2016 IEEE International Conference on Big Data (Big Data). IEEE, 34123421.Google Scholar
Mitra, B., Sural, S., Vaidya, J., and Atluri, V. (2016). “A survey of role mining.” ACM Computing Surveys (CSUR), 48(4), 137.Google Scholar
MITRE ATT&CK. (2020). ATT&CK Matrix for Enterprise. https://attack.mitre.org/. Last accessed November 2021.Google Scholar
Molina, L. C., Belanche, L., and Nebot, À. (2002). “Feature selection algorithms: a survey and experimental evaluation.” 2002 IEEE International Conference on Data Mining, 2002. ICDM 2003. Proceedings. IEEE, 306313.Google Scholar
Namayanja, J. M. and Janeja, V. P. (2014, October). “Change detection in temporally evolving computer networks: a big data framework.2014 IEEE International Conference on Big Data. IEEE, 5461.Google Scholar
Namayanja, J. M. and Janeja, V. P. (2015, May). “Change detection in evolving computer networks: changes in densification and diameter over time.” 2015 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 185187.Google Scholar
Namayanja, J. M. and Janeja, V. P. (2017). “Characterization of evolving networks for cybersecurity.” Information Fusion for Cyber-Security Analytics, Alsmadi, I., Karabatis, G, and Aleroud, A (Eds.). Studies in Computational Intelligence, vol. 691. Springer International Publishing, 111127. doi: 10.1007/978-3-319-44257-0_5.Google Scholar
Namayanja, J. M. and Janeja, V. P. (2019). “Change detection in large evolving networks.” International Journal of Data Warehousing and Mining, 15(2), 6279.Google Scholar
Naseer, S., Saleem, Y., Khalid, S., et al. (2018). “Enhanced network anomaly detection based on deep neural networks.” IEEE Access, 6, 4823148246.Google Scholar
Naus, J. (1965). “The distribution of the size of the maximum cluster of points on the line.” Journal of the American Statistical Association, 60, 532538.Google Scholar
Neill, D., Moore, A., Pereira, F., and Mitchell, T. (2005). “Detecting significant multidimensional spatial clusters.” Advances in Neural Information Processing Systems 17, MIT Press, 969976.Google Scholar
Netscout. (2020). “A global threat visualization.” www.netscout.com/global-threat-intelligence. Last accessed November 2020.Google Scholar
Ng, R. T. and Han, J. (1994, September). “Efficient and effective clustering methods for spatial data mining.” Proceedings of the 20th International Conference on Very Large Data Bases (VLDB '94). Morgan Kaufmann , 144155.Google Scholar
Ng, R. T., Lakshmanan, L. V. S., Han, J., and Pang, A. 1998. “Exploratory mining and pruning optimizations of constrained associations rules.Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data (SIGMOD '98). ACM,1324.Google Scholar
Nguyen, T. T. and Janapa Reddi, V. (2019). “Deep reinforcement learning for cyber security.” arXiv:1906.05799.Google Scholar
Nicosia, V., Tang, J., Mascolo, C., et al. (2013). “Graph metrics for temporal networks.” Temporal Networks: Understanding Complex Systems, Holme, P and Saramäki, J (Eds.). Springer, 1540. doi: 10.1007/978-3-642-36461-7_2.Google Scholar
NIST (National Institute of Standards and Technology). (2015). NIST Big Data Interoperability Framework (NBDIF), V1.0. https://bigdatawg.nist.gov/V1_output_docs.php. Last accessed April 12, 2017.Google Scholar
NIST. (2017). National vulnerability database. http://nvd.nist.gov/. Last accessed September 2017.Google Scholar
Nunes, D. S., Zhang, P., and Silva, J. S. (2015). “A survey on human-in-the-loop applications towards an internet of all.” IEEE Communications Surveys and Tutorials, 17(2), 944965.Google Scholar
O’Gorman, G. and McDonald, G. (2012). “The Elderwood Project.” www.infopoint-security.de/medien/the-elderwood-project.pdf. Last accessed November 2021.Google Scholar
Ohm, M., Sykosch, A., and Meier, M. (2020, August). “Towards detection of software supply chain attacks by forensic artifacts.Proceedings of the 15th International Conference on Availability, Reliability and Security (ARES ’20). ACM, 16. doi: 10.1145/3407023.3409183.Google Scholar
Openshaw, S. (1987). “A mark 1 geographical analysis machine for the automated analysis of point data sets.” International Journal of GIS, 1(4), 335358.Google Scholar
OSQuery. (2016). https://osquery.io/. Last accessed March 2020.Google Scholar
Otoum, S., Kantarci, B., and Mouftah, H. (2019, May). “Empowering reinforcement learning on big sensed data for intrusion detection.2019–2019 IEEE International Conference on Communications (ICC). IEEE, 17.Google Scholar
Paganini, P. (2014). “Turkish government is hijacking the IP for popular DNS providers.” http://securityaffairs.co/wordpress/23565/intelligence/turkish-government-hijacking-dns.html. Last accessed June 2017.Google Scholar
Parekh, J. J., Wang, K., and Stolfo, S. J. (2006). “Privacy-preserving payload-based correlation for accurate malicious traffic detection.” Proceedings of the 2006 SIGCOMM Workshop on Large-Scale Attack Defense (LSAD ‘06). ACM, 99106. doi: 10.1145/1162666.1162667.Google Scholar
Patcha, A. and Park, J. M. (2007). “An overview of anomaly detection techniques: existing solutions and latest technological trends.” Computer Networks, 51(12), 34483470.Google Scholar
Paul, R. J. and Taylor, S. J. E. (2002). “Improving the model development process: what use is model reuse: is there a crook at the end of the rainbow?Proceedings of the 34th Conference on Winter Simulation: Exploring New Frontiers (WSC ‘02). Winter Simulation Conference, 648652.Google Scholar
Pei, J. and Han, J. (2000). “Can we push more constraints into frequent pattern mining?” Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, 350354.Google Scholar
Peña, J. M., Lozano, J. A., and Larrañaga, P. (2002). “Learning recursive Bayesian multinets for data clustering by means of constructive induction.” Machine Learning, 47(1), 6389.Google Scholar
Pfeiffer, T., Theuerling, H., and Kauer, M. (2013). “Click me if you can! How do users decide whether to follow a call to action in an online message?” Human Aspects of Information Security, Privacy, and Trust. HAS 2013, Marinos, L and Askoxylakis, I (Eds.). Lecture Notes in Computer Science, vol. 8030. Springer, 155166. doi: 10.1007/978-3-642-39345-7_17.Google Scholar
Phillips, G., Shenker, S., and Tangmunarunkit, H. (1999). “Scaling of multicast trees: comments on the Chuang–Sirbu scaling law.Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication (SIGCOMM ’99). ACM, 4151. doi: 10.1145/316188.316205.Google Scholar
Picard, R. W. (2003). “Affective computing: challenges.” International Journal of Human-Computer Studies, 59(1), 5564.Google Scholar
Press, S. J. and Wilson, S. (1978). “Choosing between logistic regression and discriminant analysis.” Journal of the American Statistical Association, 73(364), 699705.Google Scholar
Qiu, J., Gao, L., Ranjan, S., and Nucci, A. (2007, September). “Detecting bogus BGP route information: Going beyond prefix hijacking.” In 2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops-SecureComm 2007 (pp. 381–390). IEEE.Google Scholar
Quader, F., and Janeja, V. (2014). Computational Models to Capture Human Behavior In Cybersecurity Attacks. Academy of Science and Engineering (ASE).Google Scholar
Quader, F., and Janeja, V. P. (2021). Insights into Organizational Security Readiness: Lessons Learned from Cyber-Attack Case Studies. Journal of Cybersecurity and Privacy, 1(4), 638659.Google Scholar
Quader, F., Janeja, V., and Stauffer, J. (2015, May). “Persistent threat pattern discovery.2015 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 179181.Google Scholar
Quinlan, J. R. (1979). Discovering Rules by Induction from Large Collections of Examples: Expert Systems in the Micro Electronic Age. Edinburgh University Press.Google Scholar
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.Google Scholar
Ramaswamy, S., Rastogi, R., and Shim, K. (2000, May). “Efficient algorithms for mining outliers from large data sets.” ACM Sigmod Record, 29(2), 427438).Google Scholar
Rashid, F. Y. (2013, April 4). “DHS: spear phishing campaign targeted 11 energy sector firms.” www.securityweek.com/dhs-spear-phishing-campaign-targeted-11-energy-sector-firms. Last accessed February 23, 2014.Google Scholar
Raveh, A. and Tapiero, C. S. (1980). “Periodicity, constancy, heterogeneity and the categories of qualitative time series.” Ecology, 61(3), 715719.Google Scholar
Revow, M., Williams, C. K., and Hinton, G. E. (1996). “Using generative models for handwritten digit recognition.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), 592606.Google Scholar
Risk Based Security. (2014). “A breakdown and analysis of the December, 2014 Sony hack.” www.riskbasedsecurity.com/2014/12/05/a-breakdown-and-analysis-of-the-december-2014-sony-hack/. Last accessed November 2021.Google Scholar
Rivest, R. L. and Vuillemin, J. (1976). “On recognizing graph properties from adjacency matrices.” Theoretical Computer Science, 3(3), 371384.Google Scholar
Roddick, J. F. and Hornsby, K., (Eds.). (2001). Temporal, Spatial, and Spatio-Temporal Data Mining, First International Workshop TSDM 2000 Lyon, France, September 12, 2000, Revised Papers. Lecture Notes in Computer Science, vol. 2007. Springer-Verlag.Google Scholar
Roddick, J. F., Hornsby, K., and Spiliopoulou, M. (2001). “An updated bibliography of temporal, spatial, and spatio-temporal data mining research.” Temporal, Spatial, and Spatio-Temporal Data Mining, First International Workshop TSDM 2000 Lyon, France, September 12, 2000, Revised Papers. Springer-Verlag, 147164.Google Scholar
Roddick, J. F. and Spiliopoulou, M. (1999). “A bibliography of temporal, spatial and spatio-temporal data mining research.” SIGKDD Explorations Newsletter, 1(1), 3438.Google Scholar
Roman, J The Hadoop Ecosystem Table. https://hadoopecosystemtable.github.io/. Last accessed April 13, 2017.Google Scholar
Rousseeuw, P. J. (1987). “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.Computational and Applied Mathematics. 20, 5365.Google Scholar
Sadhwani, H. (2020). “Introduction to threat hunting.” https://medium.com/@hirensadhwani2619/introduction-to-threat-hunting-8dff62ba52ca. Last accessed November 2021.Google Scholar
Sainani, H. (2018). “IP reputation scoring – a perspective on clustering with meta-features augmentation.” Thesis.Google Scholar
Sainani, H., Namayanja, J. M., Sharma, G., Misal, V., and Janeja, V. P. (2020). “IP reputation scoring with geo-contextual feature augmentation.” ACM Transactions on Management Information Systems (TMIS), 11(4), 26:1–26:29.Google Scholar
Samuel, A. W. (2004). “Hactivism and future of political participation.” www.alexandrasamuel.com/dissertation/pdfs/Samuel-Hacktivism-entire.pdf. Last accessed November 2021.Google Scholar
Sander, J., Ester, M., Kriegel, H. P., and Xu, X. (1998). “Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications.” Data Mining and Knowledge Discovery, 2(2), 169194.Google Scholar
Sandoval, G. (2008). “YouTube blames Pakistan network for 2-hour outage.” www.cnet.com/news/youtube-blames-pakistan-network-for-2-hour-outage/. Last accessed June 2017.Google Scholar
Schlamp, J., Carle, G., and Biersack, E. W. (2013). “A forensic case study on as hijacking: the attacker’s perspective.” ACM SIGCOMM Computer Communication Review, 43(2), 512.Google Scholar
Schlosser, A. E., White, T. B., and Lloyd, S. M. (2006). “Converting web site visitors into buyers: how web site investment increases consumer trusting beliefs and online purchase intentions.” Journal of Marketing, 70(2), 133148.Google Scholar
SecDev Group. (2009). “Tracking GhostNet: investigating a cyber espionage network.” www.nartv.org/mirror/ghostnet.pdf. Last accessed November 2021.Google Scholar
Shashanka, M., Shen, M., and Wang, J. (2016). “User and entity behavior analytics for enterprise security.” 2016 IEEE International Conference on Big Data (Big Data). IEEEE. 18671874. doi: 10.1109/BigData.2016.7840805.Google Scholar
Shearer, C. (2000). “The CRISP-DM model: the new blueprint for data mining.Journal of Data Warehousing, 5, 1322.Google Scholar
Shekhar, S., Lu, C., and Zhang, P. (2001). “Detecting graph-based spatial outliers: algorithms and applications.Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 371376.Google Scholar
Sheng, S., Holbrook, M., Kumaraguru, P., Cranor, L. F., and Downs, J. (2010). “Who falls for phish? A demographic analysis of phishing susceptibility and effectiveness of interventions.Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 373382.Google Scholar
Shi, L. and Janeja, V. P. (2009, June). “Anomalous window discovery through scan statistics for linear intersecting paths (SSLIP).” Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 767776.Google Scholar
Shropshire, J., Warkentin, M., Johnston, A., and Schmidt, M. (2006). “Personality and IT security: an application of the five-factor model.AMCIS 2006 Proceedings. Association for Information Systems AIS Electronic Library (AISeL), 415.Google Scholar
Simmon, E., Sowe, S. K., and Zettsu, K. (2015). “Designing a cyber-physical cloud computing architecture.” IT Professional, (3), 4045.Google Scholar
Simmons, C., Ellis, C., Shiva, S., Dasgupta, D., and Wu, Q. (2009). AVOIDIT: A cyber attack taxonomy. 9th Annual Symposium on Information Assurance, 212. https://nsarchive.gwu.edu/sites/default/files/documents/4530310/Chris-Simmons-Charles-Ellis-Sajjan-Shiva.pdf. Last accessed November 2021.Google Scholar
Skariachan, D and Finkle, J (2014). “Target shares recover after reassurance on data breach impact.” www.reuters.com/article/us-target-results/target-shares-recover-after-reassurance-on-data-breach-impact-idUSBREA1P0WC20140226. Last accessed March 2020.Google Scholar
Sklower, K. (1991, Winter). A Tree-Based Packet Routing Table for Berkeley UNIX. USENIX.Google Scholar
SMR Foundation. (2021). NodeXL. www.smrfoundation.org/nodexl/. Last accessed November 2021.Google Scholar
Snare. (2020). Snare. www.snaresolutions.com/central-83/. Last accessed March 2020.Google Scholar
Spitzner, L. (2003). Honeypots: Tracking Hackers, vol. 1. Addison-Wesley.Google Scholar
Srikant, R. and Agrawal, R. (1996). “Mining quantitative association rules in large relational tables.” Proceedings of the 1996 ACM SIGMOD international Conference on Management of Data. ACM Press, 112.Google Scholar
Statista. (2020). eCommerce report 2020 Statista Digital Market Outlook. www.statista.com/study/42335/ecommerce-report/. Last accessed November 2021.Google Scholar
Statista. (2021, March). Digital population worldwide. www.statista.com/statistics/617136/digital-population-worldwide/. Last accessed November 2021.Google Scholar
Statista. “IoT market – forecasts.” www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide/. Last accessed November 2021.Google Scholar
Stauffer, J. and Janeja, V. (2017). “A survey of advanced persistent threats and the characteristics. Technical report.Google Scholar
Stephens, G. D. and Maloof, M. A. (2014, April 22) “Insider threat detection.” U.S. Patent No. 8,707,431.Google Scholar
Stouffer, K., Falco, J., and Scarfone, K. (2009). “Guide to industrial control systems (ICS) security.” Technical report, National Institute of Standards and Technology.Google Scholar
Stubbs, J., Satter, R., and Menn, J. (2020). “U.S. Homeland Security, thousands of businesses scramble after suspected Russian hack.” www.reuters.com/article/global-cyber/global-security-teams-assess-impact-of-suspected-russian-cyber-attack-idUKKBN28O1KN. Last accessed November 2021.Google Scholar
Stutz, J. and Cheeseman, P. (1996). “AutoClass – a Bayesian approach to classification.” Maximum Entropy and Bayesian Methods. Springer, 117126.Google Scholar
Sugiura, O. and Ogden, R. T. (1994). “Testing change-points with linear trend.” Communications in Statistics – Simulation and Computation, 23(2), 287322. doi: 10.1080/03610919408813172.Google Scholar
Sugiyama, M. and Borgwardt, K. (2013). “Rapid distance-based outlier detection via sampling.” Proceedings of the 26th International Conference on Neural Information Processing Systems – Volume 1 (NIPS'13). Curran Associates, 467475.Google Scholar
Tan, Y., Vuran, M. C., and Goddard, S. (2009, June). “Spatio-temporal event model for cyber-physical systems.29th IEEE International Conference on Distributed Computing Systems Workshops, 2009. ICDCS Workshops’ 09. IEEE, 4450.Google Scholar
Tang, X., Eftelioglu, E., Oliver, D., and Shekhar, S. (2017). “Significant linear hotspot discovery.” IEEE Transactions on Big Data, 3(2), 140153.Google Scholar
Tango, T. and Takahashi, K. (2005). “A flexibly shaped spatial scan statistic for detecting clusters.” International Journal of Health Geographics, 4(11). doi: 10.1186/1476-072X-4-11.Google Scholar
Tao, F., Murtagh, F., and Farid, M. (2003). “Weighted association rule mining using weighted support and significance framework.Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’03. ACM Press, 661666.Google Scholar
Tartakovsky, A. G., Polunchenko, A. S., and Sokolov, G. (2013). “Efficient computer network anomaly detection by changepoint detection methods.” IEEE Journal of Selected Topics in Signal Processing, 7(1), 411.Google Scholar
Ten, C. W., Hong, J., and Liu, C. C. (2011). “Anomaly detection for cybersecurity of the substations.” IEEE Transactions on Smart Grid, 2(4), 865873.Google Scholar
Thakur, V. (2011, December 8). “The Sykipot Attacks.” www.symantec.com/connect/blogs/sykipot-attacks. Last accessed November 2021.Google Scholar
Tim, O., Firoiu, L., and Cohen, P. (1999). “Clustering time series with hidden markov models and dynamic time warping.” Presented at IJCAI-99 Workshop on Sequence Learning.Google Scholar
Tobler, W. R. (1970). “A computer model simulation of urban growth in the Detroit region.” Economic Geography, 46(2), 234240.Google Scholar
Townsend, M., Rupp, L., and Green, J. (2014). “Target CEO ouster shows new board focus on cyber attacks.” www.bloomberg.com/news/2014-05-05/target-ceo-ouster-shows-new-board-focus-on-cyber-attacks.html. Last accessed November 2021.Google Scholar
Trend Micro Incorporated. (2012a). ”Detecting APT activity with network traffic analysis.” www.trendmicro.com/cloud-content/us/pdfs/security-intelligence/white-papers/wp-detecting-apt-activity-with-network-traffic-analysis.pdf. Last accessed November 2021.Google Scholar
Trend Micro Incorporated. (2012b), “Spear-phishing email: most favored APT attack bait.”Google Scholar
Trinius, P., Holz, T., Göbel, J., and Freiling, F. C. (2009, October). “Visual analysis of malware behavior using treemaps and thread graphs.” 6th International Workshop on Visualization for Cyber Security, 2009. VizSec 2009. IEEE, 3338.Google Scholar
Tsuchiya, P. F. (1988). “The landmark hierarchy: a new hierarchy for routing in very large networks.” Symposium Proceedings on Communications Architectures and Protocols (SIGCOMM '88). ACM, 3542.Google Scholar
Vaarandi, R. and Podiņš, K. (2010). “Network IDs alert classification with frequent itemset mining and data clustering.2010 International Conference on Network and Service Management. IEEE, 451456.Google Scholar
Vaidya, J., Atluri, V., and Guo, Qi. (2007). “The role mining problem: finding a minimal descriptive set of roles.” Proceedings of the 12th ACM Symposium on Access Control Models and Technologies. ACM, 175184.Google Scholar
Van Mieghem, V. (2016). “Detecting malicious behaviour using system calls.” Master’s thesis, Delft University.Google Scholar
Venkatasubramanian, K., Nabar, S., Gupta, S. K. S., and Poovendran, R. (2011). “Cyber physical security solutions for pervasive health monitoring systems.” E-Healthcare Systems and Wireless Communications: Current and Future Challenges, Watfa, M (Ed.). IGI Global, 143162.Google Scholar
Verizon Wireless. (2017). “Data breach digest.” https://enterprise.verizon.com/resources/articles/2017-data-breach-digest-half-year-anniversary/. Last accessed November 2021.Google Scholar
Verma, J. P. and Patel, A. (2016, March–September). “Comparison of MapReduce and Spark programming frameworks for big data analytics on HDFS.International Journal of Computer Science and Communication, 7(2), 8084. Google Scholar
Versprille, A. (2015). “Researchers hack into driverless car system, take control of vehicle.” www.nationaldefensemagazine.org/articles/2015/5/1/2015may-researchers-hack-into-driverless-car-system-take-control-of-vehicle. Last accessed November 20201.Google Scholar
Villeneuve, N. and Sancho, D. (2011). “The ‘lurid’ downloader.” www.trendmicro.com/cloud-content/us/pdfs/security-intelligence/white-papers/wp_dissecting-lurid-apt.pdf. Last accessed November 2021.Google Scholar
Vishwanath, A., Herath, T., Chen, R., Wang, J., and Rao, H. R. (2011). “Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model.” Decision Support Systems, 51(3), 576586.Google Scholar
Wall, M. E., Rechtsteiner, A., and Rocha, L. M. (2003). “Singular value decomposition and principal component analysis.” A Practical Approach to Microarray Data Analysis, Berrar, D. P, Dubitzky, W, and Granzow, M (Eds.). Springer, 91109. doi: 10.1007/0-306-47815-3_5.Google Scholar
Wang, H., Wu, B., Yang, S., Wang, B., and Liu, Y. (2014). “Research of decision tree on YARN using MapReduce and Spark.World Congress in Computer Science, Computer Engineering, and Applied Computing. American Council on Science and Education, 2124.Google Scholar
Wang, K., He, Y., and Han, J. (2003). “Pushing support constraints into association rules mining.” IEEE Transactions Knowledge Data Engineering, 15(3), 642658.Google Scholar
Wang, K. and Stolfo, S. J. 2004. “Anomalous payload-based network intrusion detection.” Recent Advances in Intrusion Detection. RAID 2004, Jonsson, E, Valdes, A, and Almgren, M (Eds.). Lecture Notes in Computer Science, vol. 3224. Springer, 8996.Google Scholar
Wang, K., Yu, H., and Cheung, D. W. 2001. “Mining confident rules without support requirement.” Proceedings of the Tenth International Conference on Information and Knowledge Management. ACM Press, 8996.Google Scholar
Wang, L., Singhal, A., and Jajodia, S. (2007a, July). “Measuring the overall security of network configurations using attack graphs.IFIP Annual Conference on Data and Applications Security and Privacy. Springer, 98112.Google Scholar
Wang, L., Singhal, A., and Jajodia, S. (2007b, October). “Toward measuring network security using attack graphs.” Proceedings of the 2007 ACM Workshop on Quality of Protection. ACM, 4954.Google Scholar
Wang, P. A. (2011). “Online phishing in the eyes of online shoppers.” IAENG International Journal of Computer Science, 38(4), 378383.Google Scholar
Wang, Q. H. and Kim, S. H. (2009). Cyber Attacks: Cross-Country Interdependence and Enforcement. WEIS.Google Scholar
Wang, W., Yang, J., and Muntz, R. (1997, August). “STING: A statistical information grid approach to spatial data mining.VLDB, 97, 186195.Google Scholar
Ward, J. S. and Barker, A. (2013). “Undefined by data: a survey of big data definitions.” arXiv:1309.5821.Google Scholar
Websense. (2011). “Advanced persistent threat and advanced attacks: threat analysis and defense strategies for SMB, mid-size, and enterprise organizations Rev 2.” Technical report.Google Scholar
Wei, L., Keogh, E., and Xi, X. (2006). “SAXually explicit images: finding unusual shapes.” ICDM’06: Proceedings of the Sixth International Conference on Data Mining. IEEE Computer Society, 711720.Google Scholar
Weimerskirch, A. (2018). Derrick Dominic Assessing Risk: Identifying and Analyzing Cybersecurity Threats to Automated Vehicles. University of Michigan,Google Scholar
Wilson, R. J. (1986). Introduction to Graph Theory. John Wiley & Sons.Google Scholar
Wireshark. (2021). www.wireshark.org. Last accessed November 2021.Google Scholar
Wright, R., Chakraborty, S., Basoglu, A., and Marett, K. (2010). “Where did they go right? Understanding the deception in phishing communications.” Group Decision and Negotiation, 19(4), 391416.Google Scholar
Wright, R. T. and Marett, K. (2010). The influence of experiential and dispositional factors in phishing: an empirical investigation of the deceived. Journal of Management Information Systems, 27(1), 27.Google Scholar
Wu, M., Miller, R. C., and Garfinkel, S. L. (2006). “Do security toolbars actually prevent phishing attacks?Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ‘06). ACM, 601610.Google Scholar
Wu, X., Kumar, V., Quinlan, J. R., et al. (2008). “Top 10 algorithms in data mining.” Knowledge and Information Systems, 14(1), 137.Google Scholar
Wübbeling, M., Elsner, T., and Meier, M. (2014, June). “Inter-AS routing anomalies: improved detection and classification.6th International Conference on Cyber Conflict (CyCon 2014), 2014. IEEE, 223238.Google Scholar
Wybourne, M. N., Austin, M. F., and Palmer, C. C. (2009). National Cyber Security. Research and Development Challenges. Related to Economics, Physical Infrastructure and Human Behavior. I3P: Institute for Information Infrastructure Protection.Google Scholar
Xu, X., Ester, M., Kriegel, H. P., and Sander, J. (1998, February). “A distribution-based clustering algorithm for mining in large spatial databases.Proceedings, 14th International Conference on Data Engineering, 1998. IEEE, 324331.Google Scholar
Xu, X., Jäger, J., and Kriegel, H. P. (1999). “A fast parallel clustering algorithm for large spatial databases.” High Performance Data Mining., Guo, Y and Grossman, R (Eds.). Springer US, 263290.Google Scholar
Yamanishi, K. and Takeuchi, J. (2002). “A unifying framework for detecting outliers and change points from non-stationary time series data.KDD’02: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 676681.Google Scholar
Yang, S.-C., and Wang, Y.-L. (2011, May). “System dynamics based insider threat modeling.” International Journal of Network Security and Its Applications 3(3), doi: 10.1109/ICDM.2007.61.Google Scholar
Yang, X., Kong, L., Liu, Z., et al. (2018). “Machine learning and deep learning methods for cybersecurity.” IEEE Access, 6, 3536535381.Google Scholar
Yankov, D., Keogh, E., and Rebbapragada, U. (2007). “Disk aware discord discovery: finding unusual time series in terabyte sized datasets.” Seventh IEEE International Conference on Data Mining (ICDM 2007), 381390. doi: 10.1109/ICDM.2007.61.Google Scholar
Yıldırım, M. and Mackie, I. (2019). “Encouraging users to improve password security and memorability.” International Journal of Information Security, 18(6), 741759.Google Scholar
Yinka-Banjo, C. and Ugot, O.-A. (2019). “A review of generative adversarial networks and its application in cybersecurity.” Artificial Intelligence Review, 53, 17211736. doi: 10.1007/s10462-019-09717-4.Google Scholar
Yun, H., Ha, D., Hwang, B., and Ryu, K. H. (2003, September 15). “Mining association rules on significant rare data using relative support.” Journal of Systems and Software, 67(3) 181191.Google Scholar
Yanan, S., Janeja, V. P., McGuire, M. P, .and Gangopadhyay, A. (2012). “Tnet: tensor-based neighborhood discovery in traffic networks.” 2012 IEEE 28th International Conference on Data Engineering Workshops, 331336. doi: 10.1109/ICDEW.2012.72.Google Scholar
Zetter, K. (2011, April 20). “Top federal lab hacked in spear-phishing attack.” www.wired.com/threatlevel/2011/04/oak-ridge-lab-hack/. Last accessed February 23, 2014.Google Scholar
Zhang, K., Hutter, M., and Jin, H. (2009). “A new local distance-based outlier detection approach for scattered real-world data.” Advances in Knowledge Discovery and Data Mining. PAKDD 2009, Theeramunkong, T, Kijsirikul, B, Cercone, N, and Ho, T. B (Eds.). Lecture Notes in Computer Science, vol. 5476. Springer, 813822. doi: 10.1007/978-3-642-01307-2_84.Google Scholar
Zhang, P., Huang,, Y., Shekhar, S., and Kumar, V. (2003). “Correlation analysis of spatial time series datasets: a filter-and-refine approach.Advances in Knowledge Discovery and Data Mining. PAKDD 2003, Whang, K. Y., Jeon, J, Shim, K, and Srivastava, J (Eds.). Lecture Notes in Computer Science, Vol. 2637 Springer. doi: 10.1007/3-540-36175-8_53.Google Scholar
Zhang, T., Ramakrishnan, R., and Livny, M. (1996, June). “BIRCH: an efficient data clustering method for very large databases.” ACM Sigmod Record, 25(2), 103114.Google Scholar
Zhao, Q., and Bhowmick, S. S. (2003). “Sequential pattern mining: a survey.” Technical report, CAIS Nayang Technological University Singapore, 126.Google Scholar
Zhou, B., Cheung, D. W., and Kao, B. (1999, April). “A fast algorithm for density-based clustering in large database.” Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999, Zhong, N and Zhou, L (Eds.). Lecture Notes in Computer Science, Vol. 1574. Springer Berlin Heidelberg. doi: 10.1007/3-540-48912-6_45.Google Scholar
Zimmermann, A., Lorenz, A., and Oppermann, R. (2007, August). “An operational definition of context.” Modeling and Using Context. CONTEXT 2007, Kokinov, B, Richardson, D. C, Roth-Berghofer, T. R, and Vieu, L (Eds.). Lecture Notes in Computer Science, Vol. 4635. Springer Berlin Heidelberg. doi: 10.1007/978-3-540-74255-5_42.Google Scholar
Zimmermann, V. and Renaud, K. (2021). “The nudge puzzle: matching nudge interventions to cybersecurity decisions.” ACM Transactions on Computer–Human Interaction (TOCHI), 28(1), 145.Google Scholar

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  • References
  • Vandana P. Janeja, University of Maryland, Baltimore County
  • Book: Data Analytics for Cybersecurity
  • Online publication: 10 August 2022
  • Chapter DOI: https://doi.org/10.1017/9781108231954.013
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  • Vandana P. Janeja, University of Maryland, Baltimore County
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  • Book: Data Analytics for Cybersecurity
  • Online publication: 10 August 2022
  • Chapter DOI: https://doi.org/10.1017/9781108231954.013
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