A Multi-Layer Cloud-IDS Pipeline with LLM and Adaptive Q-Learning Calibration

TL;DR

This paper presents a multi-layer cloud intrusion detection system that combines machine learning, confidence calibration, and large language models to improve detection accuracy and reduce costs in cloud environments.

Contribution

It introduces an adaptive, confidence-aware multi-layer IDS with LLM integration, enhancing detection reliability and interpretability in cloud security.

Findings

Reduces LLM escalation by 58.78%

Achieves 88.68% overall detection accuracy

Maintains high accuracy at network and hypervisor layers

Abstract

Security in cloud computing has become a major concern due to several factors such as layered cloud architectures, dynamic environments, and exposure to unseen or zero-day attacks. Moreover, intrusion detection systems (IDS) typically operate at specific layers and rely heavily on machine learning models, which often perform well in experimental settings but fail to sustain performance in real cloud deployments. In this work, we implement a confidence-aware multilevel intrusion detection system using reinforcement learning tailored for cloud environments. The system secures three distinct layers: network, host, and hypervisor. Machine learning models at each layer detect known attack patterns, while prediction confidence distinguishes reliable decisions from uncertain outcomes. Within the multi-gate flow, low-confidence events pass through a learned-threshold confidence gate (Gate-1), followed by a Chroma memory-matching gate (Gate-2), with unresolved events escalated to a large language model (LLM) for semantic analysis and explanation. Final attack promotion at Gate-3 uses calibrated LLM confidence or weighted-fusion fallback, while uncertain events are retained in a review bucket to avoid forced classification. Generated explanations and confirmed knowledge are stored in ChromaDB to support future analysis and retraining. The approach is first evaluated using static thresholds, establishing a baseline for comparison. Results show that the proposed system learns adaptive thresholds and reduces LLM escalation by 58.78%, lowering cost while maintaining strong performance (88.68% accuracy, 85.29% precision, 84.72% recall, 85.00% F1). The network and hypervisor layers achieve 98.02% and 97.08% accuracy, demonstrating a balanced and efficient detection system.