Developing Visualisations to Enhance an Insider Threat Product: A Case Study

Martin Graham, Robert Kukla, Oleksii Mandrychenko, Darren Hart, Jessie B Kennedy

View presentation: 2021-10-27T17:00:00Z GMT-0600 Change your timezone on the schedule page
2021-10-27T17:00:00Z
Exemplar figure, described by caption below
Calendar view of daily alerts across a large organisation monitoring insider threat. Weekends are quiet, and a public holiday weekend especially so, but may be when a malicious user chooses to strike.
Fast forward

Direct link to video on YouTube: https://youtu.be/8OHVNhcBRtk

Keywords

Fortinet Ltd, Edinburgh

Abstract

Attack graphs (AG) are used to assess pathways availed by cyber adversaries to penetrate a network. State-of-the-art approaches for AG generation focus mostly on deriving dependencies between system vulnerabilities based on network scans and expert knowledge. In real-world operations however, it is costly and ineffective to rely on constant vulnerability scanning and expert-crafted AGs. We propose to automatically learn AGs based on actions observed through intrusion alerts, without prior expert knowledge. Specifically, we develop an unsupervised sequence learning system, SAGE, that leverages the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton(S-PDFA) – a model that accentuates infrequent severe alerts and summarizes paths leading to them. AGs are then derived from the S-PDFA. Tested with intrusion alerts collected through Collegiate Penetration Testing Competition, SAGE compresses several thousands of alerts into only a handful of AGs. These AGs reflect the strategies used by participating teams. The resulting AGs are succinct, interpretable, and enable analysts to derive actionable insights, e.g., attackers tend to follow shorter paths after they have discovered a longer one.