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Previous works on the CERT insider threat detection case have neglected graph and text features despite their relevance to describe user behavior.
A knowledge-base model for insider threat prediction. In 2007 IEEE SMC Information Assurance and Security Workshop . IEEE, 239–246
Qutaibah Althebyan and Brajendra Panda. 2007 · 2007
Earlier work this paper cites.
Using PLSI-U to detect insider threats by datamining e-mail
James S Okolica, Gilbert L Peterson, and Robert F Mills. 2008 · 2008
Earlier work this paper cites.
Insider threat detection using a graph-based approach
William Eberle, Jeffrey Graves, and Lawrence Holder. 2010 · 2010
Earlier work this paper cites.
Bayesian anomaly detection methods for social networks
Nicholas A Heard, David J Weston, Kiriaki Platanioti, and David J Hand. 2010 · 2010
Earlier work this paper cites.
Identifying and visualizing the malicious insider threat using bipartite graphs. In 2011 44th Hawaii International Conference on System Sciences . IEEE, 1–9
Kara Nance and Raffael Marty. 2011 · 2011
Earlier work this paper cites.
Insider threat detection using stream mining and graph mining. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing . IEEE, 1102–1110
Pallabi Parveen, Jonathan Evans, Bhavani Thuraisingham, Kevin W Hamlen, and Latifur Khan. 2011 · 2011
Earlier work this paper cites.
Specializing network analysis to detect anomalous insider actions
You Chen, Steve Nyemba, Wen Zhang, and Bradley Malin. 2012 · 2012
Earlier work this paper cites.
Predicting insider threat risks through linguistic analysis of electronic communication. In 2013 46th Hawaii International Conference on System Sciences . IEEE, 1849–1858
Christopher R Brown, Alison Watkins, and Frank L Greitzer. 2013 · 2013
Earlier work this paper cites.
Bridging the gap: A pragmatic approach to generating insider threat data. In 2013 IEEE Security and Privacy Workshops . IEEE, 98–104
Joshua Glasser and Brian Lindauer. 2013 · 2013
Earlier work this paper cites.
Proactive insider threat detection through social media: The YouTube case. In Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society . 261–266
Miltiadis Kandias, Vasilis Stavrou, Nick Bozovic, and Dimitris Gritzalis. 2013 · 2013
Earlier work this paper cites.
Detecting insider threats in a real corporate database of computer usage activity. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . 1393–1401
Ted E Senator, Henry G Goldberg, Alex Memory, William T Young, Brad Rees, Robert Pierce, Daniel Huang, Matthew Reardon, David A Bader, Edmond Chow, et al · 2013
Earlier work this paper cites.
Towards a User and Role-based Sequential Behavioural Analysis Tool for Insider Threat Detection
Ioannis Agrafiotis, Philip A. Legg, Michael Goldsmith, and Sadie Creese. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Web access patterns reveal insiders behavior. In 2015 Seventh International Workshop on Signal Design and its Applications in Communications (IWSDA) . IEEE, 70–74
Anagi Gamachchi and Serdar Boztaş. 2015 · 2015
Earlier work this paper cites.
Supervised and Unsupervised methods to detect Insider Threat from Enterprise Social and Online Activity Data
Gaurang Gavai, Kumar Sricharan, Dave Gunning, John Hanley, Mudita Singhal, and Rob Rolleston. 2015 · 2015
Cited alongside, same era.
Comprehensive, Multi-Source Cyber-Security Events
Alexander D Kent. 2015 · 2015
Cited alongside, same era.
Automated insider threat detection system using user and role-based profile assessment
Philip A Legg, Oliver Buckley, Michael Goldsmith, and Sadie Creese. 2015 · 2015
Cited alongside, same era.
Use of machine learning in big data analytics for insider threat detection. In MILCOM IEEE Military Communications Conference . IEEE, 915–922
Michael Mayhew, Michael Atighetchi, Aaron Adler, and Rachel Greenstadt. 2015 · 2015
Cited alongside, same era.
Towards universal paraphrastic sentence embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2015 · 2015
Cited alongside, same era.
Sedanspot: Detecting anomalies in edge streams. In 2018 IEEE International Conference on Data Mining . IEEE, 953–958
Dhivya Eswaran and Christos Faloutsos. 2018 · 2018
Later among the works it cites.
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier. In 2018 IEEE International Conference on Big Data . IEEE, 5034–5039
Adam James Hall, Nikolaos Pitropakis, William J Buchanan, and Naghmeh Moradpoor. 2018 · 2018
Later among the works it cites.
Benchmarking evolutionary computation approaches to insider threat detection. In Proceedings of the Genetic and Evolutionary Computation Conference . 1286–1293
Duc C Le, Sara Khanchi, A Nur Zincir-Heywood, and Malcolm I Heywood. 2018 · 2018
Later among the works it cites.
Evaluating insider threat detection workflow using supervised and unsupervised learning. In 2018 IEEE Security and Privacy Workshops . IEEE, 270–275
Duc C Le and A Nur Zincir-Heywood. 2018 · 2018
Later among the works it cites.
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Cyber security data sources for dynamic network research
Alexander D Kent. 2016 · 2016
Cited alongside, same era.
A scalable approach for outlier detection in edge streams using sketch-based approximations. In Proceedings of the 2016 SIAM International Conference on Data Mining . SIAM, 189–197
Stephen Ranshous, Steve Harenberg, Kshitij Sharma, and Nagiza F Samatova. 2016 · 2016
Cited alongside, same era.
A new take on detecting insider threats: exploring the use of hidden markov models. In Proceedings of the 8th ACM CCS International workshop on managing insider security threats . 47–56
Tabish Rashid, Ioannis Agrafiotis, and Jason RC Nurse. 2016 · 2016
Cited alongside, same era.
Edgecentric: Anomaly detection in edge-attributed networks. In 2016 IEEE 16th International Conference on Data Mining Workshops . IEEE, 327–334
Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Gunnemann, Disha Makhija, Mohit Kumar, and Christos Faloutsos. 2016 · 2016
Cited alongside, same era.
Detecting insider threats using radish: a system for real-time anomaly detection in heterogeneous data streams
Brock Böse, Bhargav Avasarala, Srikanta Tirthapura, Yung-Yu Chung, and Donald Steiner. 2017 · 2017
Cited alongside, same era.
Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . 1285–1298
Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar. 2017 · 2017
Cited alongside, same era.
Deep learning for unsupervised insider threat detection in structured cybersecurity data streams. In Workshops at the Thirty-First AAAI Conference on Artificial Intelligence
Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, and Sean Robinson. 2017 · 2017
Cited alongside, same era.
Towards a User and Role-Based Behavior Analysis Method for Insider Threat Detection. In 2018 International Conference on Network Infrastructure and Digital Content . IEEE, 6–10
Qiujian Lv, Yan Wang, Leiqi Wang, and Dan Wang. 2018 · 2018
Later among the works it cites.
A framework for data-driven physical security and insider threat detection. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining . IEEE, 1108–1115
Vasileios Mavroeidis, Kamer Vishi, and Audun Jøsang. 2018 · 2018
Later among the works it cites.
Anomaly-based Intrusion Detection of IoT Device Sensor Data using Provenance Graphs. In 1st International Workshop on Security and Privacy for the Internet-of-Things
Ebelechukwu Nwafor, Andre Campbell, and Gedare Bloom. 2018 · 2018
Later among the works it cites.
Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2672–2681
Wenchao Yu, Wei Cheng, Charu C Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang. 2018 · 2018
Later among the works it cites.
Insider threat detection with deep neural network. In International Conference on Computational Science . Springer, 43–54
Fangfang Yuan, Yanan Cao, Yanmin Shang, Yanbing Liu, Jianlong Tan, and Binxing Fang. 2018 · 2018
Later among the works it cites.
Insider Threat Report
Crowd Research Partners. [n. d.] · 2019
Later among the works it cites.
Embedding bag
Pytorch. [n. d.] · 2019
Later among the works it cites.
Insider Threat Test Dataset
Software Engineering Institute, Carnegie Mellon University. [n. d.] · 2019
Later among the works it cites.
Fast and accurate anomaly detection in dynamic graphs with a two-pronged approach. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 647–657
Minji Yoon, Bryan Hooi, Kijung Shin, and Christos Faloutsos. 2019 · 2019
Later among the works it cites.
Addgraph: anomaly detection in dynamic graph using attention-based temporal GCN. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . AAAI Press, 4419–4425
Li Zheng, Zhenpeng Li, Jian Li, Zhao Li, and Jun Gao. 2019 · 2019
Later among the works it cites.