HeatSense: Intelligent Thermal Anomaly Detection for Securing NoC-Enabled MPSoCs (2504.11421v1)

Abstract: Multi-Processor System-on-Chips (MPSoCs) are highly vulnerable to thermal attacks that manipulate dynamic thermal management systems. To counter this, we propose an adaptive real-time monitoring mechanism that detects abnormal thermal patterns in chip tiles. Our design space exploration helped identify key thermal features for an efficient anomaly detection module to be implemented at routers of network-enabled MPSoCs. To minimize hardware overhead, we employ weighted moving average (WMA) calculations and bit-shift operations, ensuring a lightweight yet effective implementation. By defining a spectrum of abnormal behaviors, our system successfully detects and mitigates malicious temperature fluctuations, reducing severe cases from 3.00{\deg}C to 1.9{\deg}C. The anomaly detection module achieves up to 82% of accuracy in detecting thermal attacks, which is only 10-15% less than top-performing ML models like Random Forest. However, our approach reduces hardware usage by up to 75% for logic resources and 100% for specialized resources, making it significantly more efficient than ML-based solutions. This method provides a practical, low-cost solution for resource-constrained environments, ensuring resilience against thermal attacks while maintaining system performance.

Intelligent Thermal Anomaly Detection for Securing MPSoCs

The paper "HeatSense: Intelligent Thermal Anomaly Detection for Securing NoC-Enabled MPSoCs" addresses the critical challenge of enhancing security and reliability in Multi-Processor System-on-Chips (MPSoCs) by mitigating the risks posed by thermal attacks. These attacks are particularly pernicious as they manipulate the Dynamic Thermal Management (DTM) systems crucial for maintaining safe operating temperatures within these chips. The authors propose an efficient real-time monitoring mechanism that focuses on detecting abnormal thermal patterns, exercising particular caution in resource-constrained Network-on-Chips (NoCs).

Summary of Research Contributions

The central contributions of this work can be outlined as follows:

1. Lightweight Anomaly Detection Module: The authors introduce a thermal anomaly detection system deployed at the router level of NoC-enabled MPSoCs. This module efficiently identifies malicious temperature variations by employing Weighted Moving Average (WMA) calculations and bit-shift operations, achieving an effective balance between detection accuracy and hardware efficiency.
2. Performance Metrics: The proposed system demonstrates a significant improvement in security metrics. The detection accuracy reaches up to 82%, only trailing top-performing machine learning models by 10-15%. Moreover, the hardware usage is remarkably optimized, with a reduction of up to 75% for logic resources and a complete elimination of specialized resource usage.
3. Comprehensive Simulations: The paper employs a two-stage simulation approach using the CoMeT and AccessNoxim simulators, facilitating a nuanced analysis of thermal behavior and network transactions under real-world workloads. Through this setup, the system's ability to handle uniform traffic background with realistic application benchmarks is thoroughly evaluated.
4. Temperature Management and Security Integration: The proposed method significantly attenuates temperature fluctuations induced by thermal attacks, reducing severe anomalies from 3.00°C to 1.9°C. The adaptive response mechanisms proposed for different levels of detected anomalies demonstrate practicality for real-world applications.

Implications for Future Developments

This research has several implications for future developments in the area of NoC security and thermal management:

• Integration of Advanced Anomaly Detection Techniques: While the proposed method achieves substantial efficiency gains, integrating advanced machine learning models could further improve precision and adaptability, although at potentially higher resource costs. Hybrid models combining machine learning and efficient manual heuristics may strike an advantageous balance.
• Broader Application in IoT Devices: The lightweight nature of the proposed system holds promise for integration into other Internet of Things (IoT) devices, where power and resources are even more limited than in traditional NoC environments.
• Dynamic Adaptation to Emerging Threats: Continuous refinement of the thresholding approaches and feature selection strategies can keep pace with evolving attack vectors, ensuring long-term robustness and efficacy of the anomaly detection system.

In conclusion, the paper provides a significant step forward in addressing the dual concerns of security and thermal management in MPSoCs. By applying innovative techniques for real-time anomaly detection that are both resource-efficient and effective, the proposed solution stands as a viable framework for enhancing the operational security of modern computing systems. Its implications extend beyond immediate thermal security, offering a pathway for future research and systemic improvements across a range of low-power, high-stakes computing environments.