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 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…

This paper introduces an attribution-driven analysis of encoder-based Large Language Models (LLMs) for network intrusion detection, demonstrating that the models make decisions based on meaningful tra…

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.

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…

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…

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.

FlowGuard introduces an identity-independent defense using flow matching to detect data-free model stealing attacks by identifying synthetic queries as out-of-distribution based on their lower-dimensi…

This paper proposes an explainable threat attribution system for IoT networks that uses SHAP and flow behavior modeling to accurately classify and explain over 30 distinct attack variants into 8 meani…

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.

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

The paper introduces a novel byte-level method to encode network flow records into fixed-size RGB images, significantly improving the performance of Intrusion Detection Systems (IDS) by allowing convo…

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…

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 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.

This paper develops and analyzes various ensemble models, culminating in an XGBoost-based system, to reliably detect UAV intrusions using XAI and advanced statistical methods to pinpoint the root caus…

This paper provides the first comprehensive threat model for IoT-enabled Controlled Environment Agriculture (CEA) systems, identifying 123 unique threats and proposing a defense-in-depth framework to…

FedTrident proposes a comprehensive framework to defend Federated Learning-based Road Condition Classification against Targeted Label-Flipping Attacks, achieving robust performance comparable to non-a…

NetVAD proposes a novel, identifier-free Variational Autoencoder that leverages frozen Foundation Models to achieve highly competitive unsupervised performance for zero-day intrusion detection.