Biblio

Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this work, we present a novel anomaly detection framework called TENET to detect anomalies induced by cyber-attacks on vehicles. TENET uses temporal convolutional neural networks with an integrated attention mechanism to learn the dependency between messages traversing the in-vehicle network. Post deployment in a vehicle, TENET employs a robust quantitative metric and classifier, together with the learned dependencies, to detect anomalous patterns. TENET is able to achieve an improvement of 32.70% in False Negative Rate, 19.14% in the Mathews Correlation Coefficient, and 17.25% in the ROC-AUC metric, with 94.62% fewer model parameters, and 48.14% lower inference time compared to the best performing prior works on automotive anomaly detection.

Photonic networks-on-chip (PNoCs) enable high bandwidth on-chip data transfers by using photonic waveguides capable of dense-wave-length-division-multiplexing (DWDM) for signal traversal and microring resonators (MRs) for signal modulation. A Hardware Trojan in a PNoC can manipulate the electrical driving circuit of its MRs to cause the MRs to snoop data from the neighboring wavelength channels in a shared photonic waveguide. This introduces a serious security threat. This paper presents a novel framework called SOTERIA† that utilizes process variation based authentication signatures along with architecture-level enhancements to protect data in PNoC architectures from snooping attacks. Evaluation results indicate that our approach can significantly enhance the hardware security in DWDM-based PNoCs with minimal overheads of up to 10.6% in average latency and of up to 13.3% in energy-delay-product (EDP).