Article citationsMore>>

Jiang, J., Chen, J., Gu, T., et al. (2019) Warder: Online Insider Threat Detection System Using Multi-Feature Modeling and Graph-Based Correlation. 2019 IEEE Military Communications Conference (MILCOM), Norfolk, VA, 12-14 November 2019, 1-6.
https://doi.org/10.1109/MILCOM47813.2019.9020931
https://www.researchgate.net/publication/339760983

has been cited by the following article:

• TITLE: Cybersecurity Risk Management through Behavior-Based Contextual Analysis of Online Logs

  AUTHORS: Irawati Edlabadkar, Vijay K. Madisetti

  KEYWORDS: Cyber Risk, UEBA, CVSS

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.17 No.6, June 7, 2024

  ABSTRACT: This paper studies cyber risk management by integrating contextual log analysis with User and Entity Behavior Analytics (UEBA). Leveraging Python scripting and PostgreSQL database management, the solution enriches log data with contextual and behavioral information from Linux system logs and semantic datasets. By incorporating Common Vulnerability Scoring System (CVSS) metrics and customized risk scoring algorithms, the system calculates Insider Threat scores to identify potential security breaches. The integration of contextual log analysis and UEBA [1] offers a proactive defense against insider threats, reducing false positives and prioritizing high-risk alerts.