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

Glasser, Joshua, Lindauer, Brian·2013·2013 IEEE Security and Privacy Workshops

Cited by (345)

Items 101–200 of 345

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  2. Daily insider threat detection with hybrid TCN transformer architecture
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  3. Interactive Machine Learning for Data Exfiltration Detection: Active Learning with Human Expertise
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  4. A Multi-Agent Intrusion Detection System Optimized by a Deep Reinforcement Learning Approach with a Dataset Enlarged Using a Generative Model to Reduce the Bias Effect
    Matthieu Mouyart, Guilherme Medeiros Machado, Jae‐Yun Jun · 2023 · Journal of Sensor and Actuator Networks
  5. Adaptive One-Class Ensemble-based Anomaly Detection: An Application to Insider Threats
    Diana Haidar, Mohamed Medhat Gaber · 2018 · International Joint Conference on Neural Networks (IJCNN)
  6. Context-aware graph-based analysis for detecting anomalous activities
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  7. Mitigating Insider Threats Using Bio-Inspired Models
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  8. Unsupervised Insider Detection Through Neural Feature Learning and Model Optimisation
    Liu Liu, Chao Chen, Jun Zhang, Olivier De Vel, Yang Xiang · 2019 · Lecture notes in computer science
  9. Exploring Adversarial Properties of Insider Threat Detection
    Le, Duc C., Zincir-Heywood, Nur · 2020 · 2020 IEEE Conference on Communications and Network Security (CNS)
  10. Training regime influences to semi-supervised learning for insider threat detection
    Duc C. Le, Nur Zincir-Heywood, Malcolm Iain Heywood · 2021 · IEEE Security and Privacy Workshops (SPW)
  11. RAP-Net: A Resource Access Pattern Network for Insider Threat Detection
    Dali Zhu, Xianjin Huang, Nan Li, Hongju Sun, Meichen Liu, Jiguo Liu · 2022 · International Joint Conference on Neural Networks (IJCNN)
  12. Feature Selection for Deep Neural Networks in Cyber Security Applications
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  13. Formal approach to thwart against insider attacks: A bio-inspired auto-resilient policy regulation framework
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  14. Survival analysis for insider threat
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  15. Artificial Neural Network for Cybersecurity: A Comprehensive Review
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  16. The Role of Graph Neural Networks, Transformers, and Reinforcement Learning in Network Threat Detection: A Systematic Literature Review
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  17. Deep learning methods for anomalies detection in social networks using multidimensional networks and multimodal data: a survey
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  18. Insider Threat Detection Based on User Historical Behavior and Attention Mechanism
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  19. MWCapsNet: A novel Multi-level Wavelet Capsule Network for insider threat detection using image representations
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  20. Everest: GPU-Accelerated System for Mining Temporal Motifs
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  21. Insider Threat Detection Model Enhancement Using Hybrid Algorithms between Unsupervised and Supervised Learning
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  22. A Framework to Detect the Malicious Insider Threat in Cloud Environment using Supervised Learning Methods
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  23. An insider threat detection method based on improved Test-Time Training model
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  24. Survey of Graph Neural Network for Internet of Things and NextG Networks
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  25. E-Watcher: insider threat monitoring and detection for enhanced security
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  26. SENTINEL: Insider Threat Detection Based on Multi-Timescale User Behavior Interaction Graph Learning
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  27. Notice of Removal: XABA: A zero-knowledge anomaly-based behavioral analysis method to detect insider threats
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  28. ACE – An Anomaly Contribution Explainer for Cyber-Security Applications
    Xiao Zhang, Manish Marwah, I-ta Lee, Martin F. Arlitt, Dan Goldwasser · 2019 · IEEE International Conference on Big Data (BigData)
  29. Improving Intrusion Detection Systems using Zero-Shot Recognition via Graph Embeddings
    Saber Zerhoudi, Michael Granitzer, Mathieu Garchery · 2020 · IEEE Annual International Computer Software and Applications Conference (COMPSAC)
  30. Warder: Online Insider Threat Detection System Using Multi-Feature Modeling and Graph-Based Correlation
    Jiang, Jianguo, Chen, Jiuming, Gu, Tianbo, Choo, Kim-Kwang Raymond, Liu, Chao, Yu, Min, Huang, Weiqing, Mohapatra, Prasant · 2019 · MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)
  31. Detecting insider threat within institutions using CERT dataset and different ML techniques
    Mohammed Dosh · 2021 · Periodicals of Engineering and Natural Sciences (PEN)
  32. Systematic Construction of Anomaly Detection Benchmarks from Real Data
    Andrew Emmott, Shubhomoy Das, Thomas G. Dietterich, Alan Fern, Weng‐Keen Wong · 2015
  33. An Internal Threat Detection Model Based on Denoising Autoencoders
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  34. SITD: Insider Threat Detection Using Siamese Architecture on Imbalanced Data
    Shaolei Zhou, Liming Wang, Jing Yang, Pengwei Zhan · 2022 · International Conference on Computer Supported Cooperative Work in Design (CSCWD)
  35. Cluster Aware Graph Anomaly Detection
    Lecheng Zheng, John R. Birge, Haiyue Wu, Yifang Zhang, Jingrui He · 2025 · International World Wide Web Conference (WWW)
  36. Simulated User Bots: Real Time Testing of Insider Threat Detection Systems
    Preetam K. Dutta, Gabriel Ryan, Aleksander Zieba, Salvatore J. Stolfo · 2018 · IEEE Security and Privacy Workshops (SPW)
  37. Insider Threat Detection using an Artificial Immune system Algorithm
    Obinna Igbe, Tarek Saadawi · 2018 · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
  38. Insider Intrusion Detection Techniques: A State-of-the-Art Review
    T N Nisha, Dhanya Pramod · 2023 · Journal of Computer Information Systems
  39. Prediction and Detection of Insider Threat Detection using Emails: A Comparision
    Mittal, Anupam, Garg, Urvashi · 2023 · 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)
  40. AUTH: An Adversarial Autoencoder Based Unsupervised Insider Threat Detection Scheme for Multisource Logs
    Xingjian Zhu, Jiankuo Dong, Jin Qi, Zhenguo Zhou, Zhenjiang Dong, Yanfei Sun, Wang Moyu · 2024 · IEEE Transactions on Industrial Informatics
  41. Emerging Trends and Challenges in Cybersecurity Data Science: A State-of-the-Art Review
    Nippon Datta, Tanjim Mahmud, Mohammad Tarek Aziz, Rahul Kanti Das, Mohammad Shahadat Hossain, Karl Andersson · 2024 · Parul International Conference on Engineering and Technology (PICET)
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    Lun-Pin Yuan, Euijin Choo, Ting Yu, Issa M. Khalil, Sencun Zhu · 2021 · International Conference on Dependable Systems & Networks/Proceedings
  43. Unsupervised User-Based Insider Threat Detection Using Bayesian Gaussian Mixture Models
    Simon Bertrand, Josée Desharnais, Nadia Tawbi · 2023 · Conference on Privacy, Security and Trust
  44. Contextual feature representation for image-based insider threat classification
    Shu-Min Duan, Jian-Ting Yuan, Bo Wang · 2024 · Computers & Security
  45. GMFITD: Graph Meta-Learning for Effective Few-Shot Insider Threat Detection
    Ximing Li, Linghui Li, Xiaoyong Li, Binsi Cai, Jia Jia, Yali Gao, Shui Yu · 2024 · IEEE Transactions on Information Forensics and Security
  46. Insider threat detection and its future directions
    Vrizlynn L. L. Thing, Yung Siang Liau, Dinil Mon Divakaran, Li Ling Ko · 2017 · International Journal of Security and Networks
  47. Bilateral Insider Threat Detection: Harnessing Standalone and Sequential Activities with Recurrent Neural Networks
    Phavithra Manoharan, Wei Hong, Jiao Yin, Yanchun Zhang, Wenjie Ye, Jiangang Ma · 2023 · Lecture notes in computer science
  48. Combating Insider Threats by User Profiling from Activity Logging Data
    Mohamed Dahmane, Samuel Foucher · 2018 · International Conference on Data Intelligence and Security (ICDIS)
  49. Feature Engineering Method Using Double-Layer Hidden Markov Model for Insider Threat Detection
    Xiaoyun Ye, Sung-Sam Hong, Myung-Mook Han · 2020 · International Journal of Fuzzy Logic and Intelligent Systems
  50. Abnormal Detection of Wireless Power Terminals in Untrusted Environment Based on Double Hidden Markov Model
    Kehe Wu, Jiawei Li, Bo Zhang · 2020 · IEEE Access
  51. Predicting Insider Breaches Using Employee Reviews
    Rahul Dwivedi, Sridhar Nerur, George Mangalaraj · 2023 · Journal of Computer Information Systems
  52. Detect Insider Threat with Associated Session Graph
    Junmei Ding, Peng Qian, Jing Ma, Zhiqiang Wang, Yueming Lu, Xiaqing Xie · 2024 · Electronics
  53. Multi-Granularity User Anomalous Behavior Detection
    Wenying Feng, Yu Juan Cao, Yilu Chen, Ye Wang, Ning Hu, Yan Jia, Zhaoquan Gu · 2024 · Applied Sciences
  54. A novel deep synthesis-based insider intrusion detection (DS-IID) model for malicious insiders and AI-generated threats
    Hazem M. Kotb, Tarek Gaber, Salem AlJanah, Hossam M. Zawbaa, Mohammed Alkhathami · 2025 · Scientific Reports
  55. Towards an Explainable Approach for Insider Threat Detection: Constraint Network Learning
    Orizio, Riccardo, Vuppala, Satyanarayana, Basagiannis, Stylianos, Provan, Gregory · 2020 · 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA)
  56. Advanced Persistent Threat Detection in Smart Grid Clouds Using Spatiotemporal Context-Aware Graph Embedding
    Weiyong Yang, Peng Gao, Hao Huang, Xingshen Wei, Haotian Zhang, Zhihao Qu · 2022 · Globecom
  57. Predicting User Roles from Computer Logs Using Recurrent Neural Networks
    Aaron R. Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean M. Robinson · 2017 · Proceedings of the AAAI Conference on Artificial Intelligence
  58. Data Stream Clustering for Real-Time Anomaly Detection: An Application to Insider Threats
    Diana Haidar, Mohamed Medhat Gaber · 2018 · Unsupervised and semi-supervised learning
  59. Doc2vec-Based Insider Threat Detection through Behaviour Analysis of Multi-source Security Logs
    Liu, Liu, Chen, Chao, Zhang, Jun, De Vel, Olivier, Xiang, Yang · 2020 · 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
  60. Contrastive Learning for Insider Threat Detection
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  61. Detecting abnormal logins by discovering anomalous links via graph transformers
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  62. DBWE-Corbat: Background network traffic generation using dynamic word embedding and contrastive learning for cyber range
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  63. User Behavior Analysis for Detecting Compromised User Accounts: A Review Paper
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  64. User-level malicious behavior analysis model based on the NMF-GMM algorithm and ensemble strategy
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  65. Modeling Human Behaviour with Higher Order Logic: Insider Threats
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  66. A fuzzy multicriteria aggregation method for data analytics: Application to insider threat monitoring
    Iván Palomares, Harsha Kalutarage, Yan Huang, Paul Miller Robert McCausland, Gavin McWilliams · 2017 · Joint World Congress of International Fuzzy Systems Association and International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS)
  67. Insider Threat Detection Using Generative Adversarial Graph Attention Networks
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  68. A Quantitative Framework to Model Reconnaissance by Stealthy Attackers and Support Deception-Based Defenses
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  69. An Internal/Insider Threat Score for Data Loss Prevention and Detection
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  70. Insider Threat Detection Using Bidirectional Encoder Representations From Transformers and Optimized AdaBoost Classifier
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  71. ATHITD: Attention-based temporal heterogeneous graph neural network for insider threat detection
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  72. Applying metaheuristic optimization for insider-threat detection using natural language processing
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  73. AGE: Authentication Graph Embedding for Detecting Anomalous Login Activities
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  74. Multi-source data fusion for insider threat detection using residual networks
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  75. Understanding insiders in cloud adopted organizations: A survey on taxonomies, incident analysis, defensive solutions, challenges
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  76. Detection of Temporal Data Ex-Filtration Threats to Relational Databases
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  77. Identifying and Clustering Users for Unsupervised Intrusion Detection in Corporate Audit Sessions
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  78. ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level
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  79. Using Dirichlet Marked Hawkes Processes for Insider Threat Detection
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  82. Cloud Insider Threat Detection using Deep Learning Models
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  84. NLP and Neural Networks for Insider Threat Detection
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  88. Against Insider Threats with Hybrid Anomaly Detection with Local-Feature Autoencoder and Global Statistics (LAGS)
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