Thermal Management in Large Data Centers: Security Threats and Mitigation

TL;DR

This paper examines security threats related to thermal management in large data centers, highlighting vulnerabilities in cooling systems, analyzing detection methods, and proposing a hybrid anomaly detection approach to improve safety and security.

Contribution

It introduces a novel hybrid multi-variant anomaly detection method combined with a fuzzy health factor to enhance thermal security in data centers.

Findings

Identifies security vulnerabilities in cooling systems

Evaluates current thermal anomaly detection methods

Proposes a hybrid detection approach with improved accuracy

Abstract

Data centres are experiencing significant growth in their scale, especially, with the ever-increasing demand for cloud and IoT services. However, this rapid growth has raised numerous security issues and vulnerabilities; new types of strategic cyber-attacks are aimed at specific physical components of data centres that keep them operating. Attacks against temperature monitoring and cooling systems of data centres, also known as thermal attacks, can cause a complete meltdown and are generally considered difficult to address. In this paper, we focus on this issue by analysing the potential security threats to these systems and their impact on the overall data center safety and performance. We also present current thermal anomaly detection methods and their limitations. Finally, we propose a hybrid method that uses multi-variant anomaly detection to prevent thermal attacks, as well as a fuzzy-based health factor to enhance data center thermal awareness and security