automotive security teams

Abstract: Automotive cybersecurity commonly assumes benign end-product manufacturers and design authorities, focusing on supply-chain threats, external attacks, and compromised network participants observable through misbehaviour. This assumption overlooks malicious-by-design vehicles that remain authentic and protocol-conformant while performing undeclared sensing, communication, or context-triggered actuation. We formulate an Entity-Centric Threat Model spanning three subcomponents. The first, Malign Entity Archetypes, describes four types of malign entity: peer-benign, purpose-malicious vehicles; compromised vehicles; reappropriated genuine components; and imitated vehicles or components. The model exposes two assurance gaps: authenticity does not establish declared-purpose conformance, while malicious functionality can evade detection based on behavioural history or peer-visible evidence. Reappropriated components can remain genuine and retain usable identities or capabilities outside authorised contexts, reducing the effectiveness of signal fingerprinting and certificate-based authentication. To structure this threat space, we introduce a Behaviour-Locus Matrix that maps covert, overt, and concealment actions across hardware, software, and network loci. For peer-benign, purpose-malicious vehicles, we further present a Behavioural Interaction Model that represents how hidden capabilities are contextually activated, realised through actions, manifested within the vehicle and its environment, and potentially observed or concealed.

Abstract: Threat analysis and risk assessment (TARA) according to ISO/SAE 21434 treats implementation-level hardware attacks inconsistently. We show that two of the three attack-feasibility approaches exemplified in the standard rate every attack path that requires physical access as very low by construction, independent of the implementation, while the third can assign the same path any of the four ratings, depending on the assumed state of attacker knowledge and on the attacker profile. This paper presents a hardware-aware extension of the TARA that covers side-channel analysis (SCA), fault-injection attacks (FIA), and abuse of debug and test interfaces. It adds an attacker model for physical access over the vehicle lifecycle; a path-dominance relation on the attack-potential factors that identifies when a simpler path makes a sophisticated one irrelevant; a three-level hardware relevance gate (H0 to H2) with an explicit decision rule that fixes whether resistance of a hardware step may be argued, must be documented, or must be demonstrated by test; and a rating rule under which a hardware step receives no credit for implementation-specific resistance until the required evidence exists. The gate is positioned relative to cybersecurity assurance levels and targeted attack feasibility. The method is applied to a reference gateway architecture, and its structural inputs are examined on ten published attacks on automotive components. In seven of the ten studies, the demonstrated attack included a debug, programming, boot, diagnostic, or update mechanism, whereas only four studies included an attack on a cryptographic computation.

Abstract: Automotive Ethernet carries heterogeneous multi-protocol traffic in modern in-vehicle networks, where labeled attack data are rarely available and the strongest prior unsupervised detector still relies on handcrafted traffic features. This article presents X-SPUR, an explainable, surprisal-based, protocol-aware unsupervised reasoning framework that instead represents raw packet fields as token sequences, learns benign traffic patterns through causal language modeling, and detects anomalies from per-token cross-entropy surprisal. To incorporate temporal context, we introduce a bimodal fusion architecture that combines payload-token embeddings with inter-packet timing through additive fusion and a Hadamard interaction. To handle the heterogeneous score distributions of different protocol families, we further propose a dual top-$k$% per-protocol $Z$-score calibration that jointly captures moderately distributed and sparse anomaly signatures. On the TOW-IDS dataset, X-SPUR achieves an AUC of 0.9987. This is marginally higher than the 0.9969 reported for AERO. X-SPUR also eliminates handcrafted feature engineering. We train a separate CarDS model using the same architecture and training hyperparameters. This model retains strong performance on the second automotive Ethernet dataset. Beyond detection, per-token surprisal provides fine-grained explainability by attributing anomaly scores to specific protocol fields, supporting interpretable security analysis in heterogeneous in-vehicle networks.