C-MADF learns a structural causal model to restrict response actions in an MDP and uses dual blue-red RL policies to achieve 1.8% false-positive rate and 0.979 F1 on the CICIoT2023 dataset.
Chatnvd: Advancing cybersecurity vulnerability assessment with large language models,
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cs.CR 2years
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AgenticVM reduces vulnerability scanner alerts by up to 98% and predicts missing CVSS attributes with 89.3% accuracy using a multi-agent LLM framework integrated with security tools and public databases.
citing papers explorer
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Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning
C-MADF learns a structural causal model to restrict response actions in an MDP and uses dual blue-red RL policies to achieve 1.8% false-positive rate and 0.979 F1 on the CICIoT2023 dataset.
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AgenticVM: Agentic AI for Adaptive Software Vulnerability Management
AgenticVM reduces vulnerability scanner alerts by up to 98% and predicts missing CVSS attributes with 89.3% accuracy using a multi-agent LLM framework integrated with security tools and public databases.