Neural-network-based hardware trojan attack prediction and security defense mechanism in optical networks-on-chip

Abstract

Optical networks-on-chip (ONoCs) have emerged as a compelling platform for many-core systems owing to their notable attributes, including high bandwidth, low latency, and energy efficiency. Nonetheless, the integration of microring resonators (MRs) in ONoCs exposes them to vulnerabilities associated with hardware trojans (HTs). In response, we propose an innovative strategy that combines deep-learning-based HT attack prediction with a robust security defense mechanism to fortify the resilience of ONoCs. For HT attack prediction, we employ a multiple-inputs and multiple-outputs long short-term memory neural network model. This model serves to identify susceptible MRs by forecasting alterations in traffic patterns and detecting internal faults within optical routing nodes. On the defensive front, we introduce a fine-grained defense mechanism based on MR faults. This mechanism effectively thwarts HTs during the optical routing process, thereby optimizing node utilization in ONoCs while concurrently upholding security and reliability. Simulation outcomes underscore the efficacy of the proposed HT attack prediction mechanism, demonstrating high accuracy with a loss rate of less than 0.7%. The measured mean absolute error and root mean squared error stand at 0.045 and 0.07, respectively. Furthermore, when compared to conventional coarse-grained node-based defense algorithms, our solution achieves noteworthy reductions of up to 16.2%, 43.72%, and 44.86% in packet loss rate, insertion loss, and crosstalk noise, respectively.