LLM and Q-learning enhance cloud intrusion detection system

Researchers have developed a novel multi-layer intrusion detection system (IDS) for cloud environments that integrates large language models (LLMs) and adaptive Q-learning. This system operates across network, host, and hypervisor layers, using machine learning models for initial detection and confidence scores to manage uncertain predictions. Low-confidence events are processed through multiple gates, with unresolved cases escalated to an LLM for semantic analysis, ultimately improving detection accuracy and efficiency. AI

影响 This new system could improve the security and efficiency of cloud infrastructure by better detecting and managing threats.

排序理由 Academic paper detailing a new technical approach. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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