The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…

The FALCON-C framework proposes a flow-based autoencoder approach to detect cyber anomalies and label malicious flows in connected vehicular networks, achieving high accuracy in identifying attacks on…

The paper proposes a trust-aware federated hybrid intrusion detection framework using multiple ML models at distributed edge nodes to proactively secure highly connected Intelligent Transport Systems.

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 proposes a hybrid CNN-LSTM framework to enhance cyber attack detection and prevention in U.S. critical digital infrastructure by evaluating multiple machine learning models on the CSE-CIC-I…

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 proposes a Cycle-Space Detector (CSD) that uses network topology constraints to effectively detect stealthy, data-driven False Data Injection Attacks (FDIA) that exploit the null space of me…

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…

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 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…

This paper identifies and demonstrates eight novel attack scenarios exploiting the ISO 15765-2 transport protocol over CAN, showing that three can successfully induce denial of diagnostic services in…

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 enhances anomaly detection and threat intelligence in Zero Trust IoT environments by applying and comparing various machine learning classifiers, notably using SMOTE to improve accuracy on…

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 CyberAId, a hybrid multi-agent system designed to enhance cybersecurity for financial institutions by integrating specialized LLM subagents with existing SIEM/XDR telemetry, address…

The paper proposes EnThM, a lightweight, hierarchical verification scheme that uses statistical and rule-based checks on aggregated metering data to mitigate real-time power theft in smart grids.

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.

This paper synthesizes the emerging field of blockchain and AI for securing intelligent networks by providing a comprehensive taxonomy, integration patterns, and an evaluation blueprint.

This survey comprehensively analyzes the IoT threat landscape by detailing 28 common attacks and mapping them to foundational vulnerability classes, providing a structured roadmap for building secure…

This paper systematically evaluates Provenance-based Intrusion Detection Systems (PIDSes) in real industrial scenarios, revealing that existing systems struggle with data heterogeneity, advanced attac…