The paper proposes CANGuard, a hybrid CNN-GRU-Attention deep learning model, to accurately detect sophisticated Denial-of-Service and spoofing attacks targeting critical in-vehicle CAN bus networks.

The paper proposes a proactive, resilient architecture for autonomous vehicles by integrating redundancy, diversity, and adaptive reconfiguration to defend against various cyber and physical attacks.

The paper proposes DAIRE, a lightweight AI model, for highly efficient, real-time detection and classification of various cyberattacks targeting the vulnerable Controller Area Network (CAN) in the Int…

The paper introduces a novel toolkit to enhance RISC-V Trusted Execution Environments (TEEs) by adding modular extensions for secure enclave update, migration, state continuity, and trusted time, ther…

The paper introduces a novel pipeline integrating formal verification and process mining to systematically identify and analyze root causes of security property invalidations in complex automotive net…

The paper demonstrates that using lightweight block cipher encryption on CAN payloads can effectively prevent passive reverse engineering of signal semantics without significantly impacting real-time…

This paper surveys how integrating Edge Computing, Machine Learning, and Deep Learning can enhance the security and resilience of complex Internet of Vehicles (IoV) networks.

This paper discusses the significant challenges in developing a holistic intrusion detection system for Industrial Control Systems (ICS) that must cover all operational dimensions.

The paper introduces CAN-QA, a novel question-answering benchmark that reformulates CAN traffic analysis from a classification task to a reasoning task, demonstrating that current LLMs struggle with c…

This paper demonstrates that Unmanned Aerial Vehicle (UAV) autopilot fail-safe mechanisms are vulnerable to non-invasive voltage glitch fault injection, potentially allowing attackers to suppress crit…

The paper introduces APIOT, the first LLM framework capable of autonomously performing the full discovery, exploitation, patching, and verification cycle against bare-metal industrial OT devices.

This paper reviews the current state of cybersecurity for EV charging infrastructure, analyzing existing machine learning countermeasures and proposing future directions to overcome data limitations i…

The paper introduces PLM-NIDS, a novel intrusion detection system that models network flows as a language based solely on L3/L4 metadata, successfully detecting attacks by identifying deviations from…

The paper introduces PLM-NIDS, a novel intrusion detection system that models network flows as a language based solely on L3/L4 metadata, successfully detecting attacks by identifying deviations from…

This paper investigates the vulnerability of machine learning-based fault detection and localization systems in Cyber-Physical Systems (CPS) to backdoor attacks, demonstrating that such attacks are su…

immUNITY is a system that enhances network security by combining programmable switches and SmartNICs to efficiently detect and mitigate low-volume and slow network attacks.

The paper identifies a critical vulnerability, the Camouflage Detection Gap (CDG), where standard LLM injection detectors fail dramatically when malicious payloads mimic the target domain's language a…

This paper demonstrates that an off-the-shelf Large Language Model (LLM) can function as a high-performing, explainable, human-in-the-loop layer for detecting cyberattacks in Industrial Control System…

This paper evaluates the security of industrial control systems (ICS) transitioning to 5G communication, finding that while optimal conditions allow for resilience, degraded channel conditions signifi…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.