📙 Conducting the Kill-Chain: Detecting APT Progression Through Music-Sequence Modeling 🤖
summary
Sequence-aware detection engine uses music-inspired encoding, transformer attention and motive detection to score event progressions as harmonic dissonance, implemented as a SIM-agnostic precorrelation layer that outputs ordered sequences for analysts. Evaluated on lateral-movement and Shadow SaaS cases, the prototype flags attacks about 11 minutes earlier, improves meaningful alerts ~15% and cuts false positives ~22%, reducing hosts to investigate and enabling earlier containment before data exfiltration.
AI-written summary · about
chapters (10)
- 0:00Introduction to APT detection
- 3:04Problem with isolated security events
- 7:16Music analogy for attack sequences
- 10:47Harmonic discordance in detections
- 13:17Predictive defense with transformers
- 15:26Case study lateral movement
- 20:20Case study Shadow SaaS
- 23:13Impact and architectural fit
- 25:00Lessons learned and future work
- 26:45Final takeaway and Q&A
related talks
50mUsing An Expanded Cyber Kill Chain Model to Increase Attack Resiliency
38mAdversary Detection Pipelines: Finally Making Your Threat Intel Useful
31mAdversary Emulation: Generating MITRE ATT&CK Technique Sequences
49mFight Smarter: Accelerate Your SOC with AI Discovered Attacks
53mAI-equipped Threat Actors Versus AI-enhanced Cyber Tools: Who Wins?
13mAPTGen: An Approach towards Generating Practical Dataset Labelled with Targeted...
20mDynamic Defense: Crafting threat models with real-world Intelligence
50mMalice In Chains: Supply Chain Attacks Using Machine Learning Models