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About this paper
Nnamdi Philip Okonkwo, Lubna Luxmi Dhirani
Researchers at the University of Limerick developed a lightweight AI-Augmented Security Operations Center (AISOC) architecture for cloud environments, integrating fusion-based machine learning models for malware and log anomaly detection. This system effectively classified cloud activity into three triage levels, achieving a macro-F1 score of 1.00 in a simulated reverse-shell attack testbed.
Key insights
- Fusion of malware detection and log behavior analysis significantly enhances threat detection coverage by leveraging complementary error modes of individual detectors.
- The use of adversarial log augmentation, including keyword obfuscation and noise injection, improves the robustness of log anomaly detection models against evasion tactics.
- A calibrated dual-threshold fusion mechanism provides clear, actionable triage levels, simplifying alert prioritization for security analysts in resource-constrained environments.
Summary by alphaXiv.
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