This work presents a white-box modeling of the electromagnetic (EM) leakage from an integrated circuit (IC) to develop EM side-channel analysis (SCA)-aware design techniques. A new digital library cell layout design technique is proposed to minimize the EM leakage and is evaluated using a high-frequency structure simulator (HFSS)-based framework. Backed by our physics-based understanding of EM radiation, the proposed double-row power grid-based digital cell layout design shows$>5\times $reduction in the EM SCA leakage compared to the traditional digital logic gate layout design. Furthermore, exploiting the magneto-quasistatic (MQS) regime of operation of the EM leakage from the CMOS circuits, the HFSS-based framework is utilized to develop a pre-silicon (Si) EM SCA evaluation technique to assess the vulnerability of cryptographic implementations against such attacks during the design phase itself.

Recently, Collective Perception Messages (CPM) that carry additional information about the surrounding environment beyond Basic Safety Messages (BSM) or Cooperative Awareness Messages (CAM) have been proposed to increase the situational awareness for Connected and Automated Vehicles (CAV) in Intelligent Transportation Systems. However, blindly trusting perception information from neighbors that cannot be locally verified is dangerous given the safety impact that erroneous or malicious information might have. This paper addresses the data trust challenge of CPMs, proposing a misbehavior detection scheme called MISO- V (Multiple Independent Sources of Observations over V2X) that leverages the inherently overlapping nature of the perception observations from multiple vehicles to verify the semantic correctness of the V2X data and improve the data trust and robustness of V2X systems. CPM-enabled CAVs are implemented and MISO-V performance is evaluated in CARLA-based simulation tool, where falsified V2X packets presenting a ghost car are injected in a suburban T-junction scenario with other cars. The results show that MISO- V is very effective in detecting the ghost car attacks and removing the impact of such misbehavior from influencing the receiver and offers a conservative and sensible approach towards trustworthy Collective Perception Services for CAV s.

Remote attestation is a well-established interactive technique to establish trust in the realm of connected devices. It allows a Prover device to attest its platform integrity to a Verifier device. Existing remote attestation protocols rely on classical asymmetric cryptography, which are too heavy for low-end Prover devices, and vulnerable to quantum attacks (a serious concern due to the long lifespan of IoT devices). Hash-Based Signatures (HBS) offer attractive performance and have capabilities to defeat quantum attacks. This paper presents several contributions in this context. First, we present an efficient remote attestation protocol that requires the Prover to perform only one-time HBS operations, which are very lightweight. Our protocol also proposes robust embedded techniques to refresh one-time keys that allow multiple attestations. Second, we present a simpler construction based on multi-time HBS scheme which does not depend on a trusted-third-party. Third, to demonstrate the feasibility of our protocols, we developed prototypes based on state-of-the-art HBS schemes (XMSS and WOTS+) for highly constrained platforms (Arduino 101) in both classical and post-quantum security settings. Finally, we present a comprehensive comparison between these strategies, including guidance on suitable use cases for each one of them. To summarize, our work demonstrates the feasibility of modern HBS constructions for remote attestation of highly-constrained devices.

The increasing utilization of Electronic Control Units (ECUs) and wireless connectivity in modern vehicles has favored the emergence of security issues. Recently, several attacks have been demonstrated against in-vehicle networks therefore drawing significant attention. This paper presents an Intrusion Detection System (IDS) based on a regression learning approach which estimates certain parameters by using correlated/redundant data. The estimated values are compared to observed ones to identify abnormal contexts that would indicate intrusion. Experiments performed with real-world vehicular data have shown that more than 90% of vehicle speed data can be precisely estimated within the error bound of 3 kph. The proposed IDS is capable of detecting and localizing attacks in real-time, which is fundamental to achieve automotive security.