Anomaly Detection and Early Warning Mechanism for Intelligent Monitoring Systems in Multi-Cloud Environments Based on LLM

2025-06-09

AI Summary

To address security vulnerabilities, low reliability, and delayed anomaly response in intelligent monitoring systems deployed across multi-cloud environments, this paper proposes a cross-cloud anomaly detection and early-warning framework that synergistically integrates large language models (LLMs) with traditional machine learning. The method innovatively leverages LLMs’ contextual understanding to enhance semantic representation in multi-level feature extraction and enables dynamic system modeling and latent failure prediction. Furthermore, lightweight adaptation and seamless integration with existing multi-cloud monitoring infrastructure ensure real-time performance and practical deployability. Experimental evaluation demonstrates that the proposed approach achieves a +12.7% improvement in detection accuracy and reduces end-to-end latency by 43.5%, significantly strengthening cloud infrastructure resilience and enabling proactive operations and maintenance.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Anomaly/Outlier DetectionIntelligent Robots: Multi-Robot Systems

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

With the rapid development of multi-cloud environments, it is increasingly important to ensure the security and reliability of intelligent monitoring systems. In this paper, we propose an anomaly detection and early warning mechanism for intelligent monitoring system in multi-cloud environment based on Large-Scale Language Model (LLM). On the basis of the existing monitoring framework, the proposed model innovatively introduces a multi-level feature extraction method, which combines the natural language processing ability of LLM with traditional machine learning methods to enhance the accuracy of anomaly detection and improve the real-time response efficiency. By introducing the contextual understanding capabilities of LLMs, the model dynamically adapts to different cloud service providers and environments, so as to more effectively detect abnormal patterns and predict potential failures. Experimental results show that the proposed model is significantly better than the traditional anomaly detection system in terms of detection accuracy and latency, and significantly improves the resilience and active management ability of cloud infrastructure.

Problem

• Detect anomalies in multi-cloud monitoring systems using LLM
• Enhance detection accuracy with multi-level feature extraction
• Adapt dynamically to diverse cloud environments for early warnings

Innovation

• LLM-based multi-level feature extraction
• Dynamic adaptation to multi-cloud environments
• Combines NLP with traditional machine learning