Article

Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data

Authors:

Joshua Glasser,

Brian LindauerAuthors Info & Claims

SPW '13: Proceedings of the 2013 IEEE Security and Privacy Workshops

Pages 98 - 104

https://doi.org/10.1109/SPW.2013.37

Published: 23 May 2013 Publication History

Abstract

The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hinderance. We outline the use of synthetic data to enable progress in one research program, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, as well as future research directions.

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Zhang BGao YKuang BYu CFu ASusilo W(2024)A Survey on Advanced Persistent Threat Detection: A Unified Framework, Challenges, and CountermeasuresACM Computing Surveys10.1145/370074957:3(1-36)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3700749
Buchta RGkoktsis GHeine FKleiner C(2024)Advanced Persistent Threat Attack Detection Systems: A Review of Approaches, Challenges, and TrendsDigital Threats: Research and Practice10.1145/36960145:4(1-37)Online publication date: 17-Sep-2024
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Published In

SPW '13: Proceedings of the 2013 IEEE Security and Privacy Workshops

May 2013

180 pages

ISBN:9780769550176

Publisher

IEEE Computer Society

United States

Publication History

Published: 23 May 2013

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Zhang BGao YKuang BYu CFu ASusilo W(2024)A Survey on Advanced Persistent Threat Detection: A Unified Framework, Challenges, and CountermeasuresACM Computing Surveys10.1145/370074957:3(1-36)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3700749
Buchta RGkoktsis GHeine FKleiner C(2024)Advanced Persistent Threat Attack Detection Systems: A Review of Approaches, Challenges, and TrendsDigital Threats: Research and Practice10.1145/36960145:4(1-37)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.1145/3696014
Gonçalves LZanchettin C(2024)Detecting abnormal logins by discovering anomalous links via graph transformersComputers and Security10.1016/j.cose.2024.103944144:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.cose.2024.103944
Al-Mhiqani MAlsboui TAl-Shehari TAbdulkareem KAhmad RMohammed M(2024)Insider threat detection in cyber-physical systemsComputers and Electrical Engineering10.1016/j.compeleceng.2024.109489119:PAOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109489
Jurišić MTomičić IGrd P(2023)User Behavior Analysis for Detecting Compromised User AccountsCybernetics and Information Technologies10.2478/cait-2023-002723:3(102-113)Online publication date: 1-Sep-2023
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Yuan YYe HVedula SKaza WTalati N(2023)Everest: GPU-Accelerated System for Mining Temporal MotifsProceedings of the VLDB Endowment10.14778/3626292.362629917:2(162-174)Online publication date: 1-Oct-2023
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King IHuang H(2023)Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionACM Transactions on Privacy and Security10.1145/358877126:3(1-36)Online publication date: 27-Jun-2023
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Li XLi XJia JLi LYuan JGao YYu S(2023)A High Accuracy and Adaptive Anomaly Detection Model With Dual-Domain Graph Convolutional Network for Insider Threat DetectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.324541318(1638-1652)Online publication date: 1-Jan-2023
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Curiosity Killed the Organization: A Psychological Comparison between Malicious and Non-Malicious Insiders and the Insider Threat

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