AWARe-Wi: A jamming-aware reconfigurable wireless interconnection using adversarial learning for multichip systems

摘要

Performance of the compute-intensive multichip platforms such as micro-servers and embedded systems are limited by the latency and power hungry chip-to-chip interconnections. Millimeter wave (mm-wave) wireless interconnection networks have emerged as an energy-efficient and low-latency solution for such multichip system communication. We refer such multichip systems with in-package mm-wave wireless interconnect as Wireless Network-in-Package (WiNiP). Despite providing performance enhancements, wireless channel, being an unguided medium, introduces potential security vulnerabilities inherited from traditional wireless networks such as jamming induced Denial-of-Service (DoS) and eavesdropping. Securing the systems against such induced threats often introduce large overheads and performance penalties. To address these challenges, we propose a WiNiP architecture that reuses the in-built Design for Testability (DFT) hardware for securing against external and Hardware Trojans (HT) induced internal attacks. The proposed architecture is capable of securing against adversaries with a reconfigurable wireless interconnection (AWARe-Wi). We deploy machine learning (ML) classifier to detect the threats. In addition, for a robust threat detection, we introduce an Adversarial ML (AML) based approach in this work. To enable sustainable multichip communication in such systems even under jamming attack from both internal and external attackers, we design a reconfigurable Medium Access Control (MAC) and a suitable communication protocol. The simulation results show that, the ML and AML classifiers can achieve an accuracy of 99.87% and 95.95% respectively for attack detection while the proposed WiNiP can sustain chip-to-chip communication even under persistent jamming attack with an average 1.44? and 1.56? degradation in latency for internal and external attacks respectively for application-specific traffic patterns.