Agentic SABRE: An Uncertainty-Aware Neuro-Symbolic Multi-Agent Framework for Adaptive Ransomware Detection
A neuro-symbolic multi-agent framework tackles ransomware detection under concept drift by coupling uncertainty quantification with symbolic reasoning — relevant for SOCs struggling with polymorphic variants that bypass static classifiers.
Summary written by editorial AI · Source link below
arXiv:2607.04292v1 Announce Type: cross Abstract: Ransomware has evolved into a complex, adaptive, and fast-moving adversary category in which static signatures and monolithic classifiers fail to generalise under concept drift, evasion, and behavioural polymorphism. In this paper, we present Agentic SABRE (Semantic-Behavioural Arbitration for Ransomware Evaluation), an uncertainty-aware, neuro-symbolic, multi-agent framework for adaptive ransomware detection. SABRE fuses semantic, representatio
Editorial Analysis
As ransomware families increasingly use behavioural polymorphism to evade ML-based detection, frameworks that quantify model uncertainty could reduce false-negative rates in production SOC environments.
Track this research for potential integration into next-gen ransomware detection pipelines; compare its concept-drift resilience claims against your current EDR tooling.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the Research Desk
- Is That Really My X-Ray? Measuring Internet-Exposed DICOM Services in the Presence of Deception20 Jul
- Characterizing Phishing Pages by JavaScript Capabilities20 Jul
- Intentional Electromagnetic Interference Attacks on Facial Recognition20 Jul
- DoSQ: A Cross-Layer Denial of Service Quality Attack by Exploiting Side Channels in 5G NR20 Jul
- Vogls: a Fast Interactive Full-timing Simulator for Pre-silicon Power Side-Channel Analysis20 Jul