Biblio

Compute-intensive platforms such as micro-servers and embedded systems have already undergone a shift from a single-chip to multichip architecture to achieve better yield and lower cost. However, performance of multichip systems is limited by the latency and power-hungry chip-to-chip wired I/Os. On the other hand, wireless interconnections are emerging as an energy-efficient and low latency interconnect solution for such multichip systems as it can mask long multi-hop off-chip wired I/O communication. Despite efficient communication, the unguided on and off-chip wireless communication introduce security vulnerabilities in the system. In this work, we propose a reconfigurable, secure millimeter-wave (mm-Wave) wireless interconnection architecture (AReS) for multichip systems capable of detecting and defending against emerging threats including Hardware Trojans (HTs) and Denial-of-Service (DoS) using a Machine Learning (ML)-based approach. The ML-based approach is used to classify internal and external attack to enable the required defense mechanism. To serve this purpose, we design a reconfigurable Medium Access Control (MAC) and a suitable communication protocol to enable sustainable communication even under jamming attack from both internal and external attackers. The proposed architecture also reuses the in-built test infrastructure to detect and withstand a persistent jamming attack in a wireless multichip system. Through simulation, we show that, the proposed wireless interconnection can sustain chip-to-chip communication even under persistent jamming attack with an average 1.44xand 1.56x latency degradation for internal and external attacks respectively for application-specific traffic.

Wireless Networks-on-Chips (NoCs) have emerged as a panacea to the non-scalable multi-hop data transmission paths in traditional wired NoC architectures. Using low-power transceivers in NoC switches, novel Wireless NoC (WiNoC) architectures have been shown to achieve higher energy efficiency with improved peak bandwidth and reduced on-chip data transfer latency. However, using wireless interconnects for data transfer within a chip makes the on-chip communications vulnerable to various security threats from either external attackers or internal hardware Trojans (HTs). In this work, we propose a mechanism to make the wireless communication in a WiNoC secure against persistent jamming based Denial-of-Service attacks from both external and internal attackers. Persistent jamming attacks on the on-chip wireless medium will cause interference in data transfer over the duration of the attack resulting in errors in contiguous bits, known as burst errors. Therefore, we use a burst error correction code to monitor the rate of burst errors received over the wireless medium and deploy a Machine Learning (ML) classifier to detect the persistent jamming attack and distinguish it from random burst errors. In the event of jamming attack, alternate routing strategies are proposed to avoid the DoS attack over the wireless medium, so that a secure data transfer can be sustained even in the presence of jamming. We evaluate the proposed technique on a secure WiNoC in the presence of DoS attacks. It has been observed that with the proposed defense mechanisms, WiNoC can outperform a wired NoC even in presence of attacks in terms of performance and security. On an average, 99.87% attack detection was achieved with the chosen ML Classifiers. A bandwidth degradation of \textbackslashtextless;3% is experienced in the event of internal attack, while the wireless interconnects are disabled in the presence of an external attacker.