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…

The paper proposes a hybrid ASIC+eFPGA architecture to enhance the security and resilience of edge LLM inference accelerators against both runtime and supply-chain attacks.

This paper experimentally demonstrates that IEC 61850 Sampled Values-based protection systems are vulnerable to stealthy, coordinated False Data Injection Attacks (FDIAs) that can disrupt grid protect…

This paper proposes a comprehensive framework for network intrusion detection using unified multi-modal datasets and evaluates advanced adversarial learning methods for generating high-fidelity synthe…

This paper demonstrates that neural operators used in digital twins for nuclear systems are highly vulnerable to undetectable, sparse adversarial perturbations, necessitating new robustness guarantees…

AEGIS introduces a novel physics-based system that analyzes encrypted network traffic flow dynamics, achieving state-of-the-art zero-day evasion detection with high accuracy and low latency.

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 investigates how Generative AI enables scalable, hyper-realistic fraud in Chinese e-commerce by fabricating product defect evidence, proposing new defense mechanisms like verifiable materia…

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 evaluates quantum machine learning for detecting anomalies in UAVs using a rigorous, leakage-free methodology, showing that a hybrid XGBoost + Data Reuploading classifier performs well, part…

The paper proposes a novel three-layer metric framework to comprehensively evaluate quantum circuit integrity by combining structural, operational, and interaction-level analyses, demonstrating that n…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…

The paper demonstrates that simpler, shallower Deep Neural Network architectures with reduced features and ReLU activations can inherently improve the robustness of ML-NIDS against gradient-based adve…

The paper proposes PCDM, a diffusion-based framework that enables highly stealthy and effective data poisoning attacks against Federated Learning systems, significantly degrading global performance wh…

The paper proposes AEGIS, a novel diffusion-guided method for injecting adversarial perturbations into the latent space to create generalizable and robust defenses against advanced facial deepfake man…

The paper introduces a Spatiotemporal-Aware Fault Injection (STAFI) framework to efficiently locate and time critical bit-flip vulnerabilities in DNNs used for ADAS, significantly improving fault dete…

The paper proposes an AI-based supervisory layer using a recurrent neural network to validate the physical integrity of current measurements used by line current differential relays in inverter-based…

The paper introduces ParDef, a generalized defense mechanism that effectively mitigates various types of parameter attacks on deep neural networks while maintaining high performance.

NeuroTrace introduces a novel framework using Inference Provenance Graphs (IPGs) to analyze the information flow during deep neural network inference, demonstrating that this provenance provides a rob…