arXiv AI
Jun 3
AI Agents Enable Adaptive Computer Worms
arXiv:2606. 03811v1 Announce Type: cross Abstract: A computer worm is malware that spreads on a network by replicating itself from one machine to another.
Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense
arXiv:2512. 23849v2 Announce Type: replace-cross Abstract: Sophisticated attackers can evade detection-based security by using encryption, stealth tactics, and low-rate attack patterns.
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arXiv AI
Aug 21
Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection
arXiv:2403. 15509v3 Announce Type: replace-cross Abstract: Knowledge distillation (KD) has been widely used to improve lightweight AI models by transferring soft-label knowledge from a large teacher model to a student model.
arXiv AI
Jun 15
From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails
arXiv:2606. 14517v1 Announce Type: cross Abstract: LLM-based guardrails have emerged as a highly effective defense against prompt injection and jailbreak attacks in autonomous agents.
arXiv Machine Learning
Aug 12
Beyond Detection Accuracy: Measuring Explanation Cost, Stability, and Utility for Resource-Aware IoT Intrusion Detection
arXiv:2608. 10349v1 Announce Type: cross Abstract: Machine-learning intrusion-detection studies commonly emphasize predictive accuracy while treating explanation generation as a computationally free post-processing step.
arXiv AI
Jun 2
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
arXiv:2606. 00134v1 Announce Type: cross Abstract: Intrusion Detection Systems (IDS) in Internet of Things (IoT) environments face significant challenges due to data heterogeneity, lack of labeled data, and limited model interpretability.
arXiv Computation and Language
Aug 25
Decomposition Attacks Across Unlinkable Identities: Limits of Stateful Defenses for LLM Services
arXiv:2608.17445v2 Announce Type: replace-cross Abstract: Most large language model services use stateless defenses, which judge only the current request, to refuse harmful tasks. Decomposition attac...
arXiv Machine Learning
Sep 25
Unmasking Shortcut Learning in IoT Intrusion Detection: A Forensic, Multi-Paradigm Evaluation of Feature Dependence and Data Leakage
The paper investigates whether machine learning models for IoT intrusion detection truly learn attack patterns or rely on dataset shortcuts. Using the CyberFlowIoT-GICAP benchmark, the authors evaluate four learning paradigms across different feature sets and split strategies, finding that performance is largely driven by feature representation and that tree-based models can exploit temporal artifacts. The study also highlights asymmetric attack detectability and proposes a four-point protocol checklist for realistic evaluation.
arXiv Machine Learning
Jul 21
A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Traffic
arXiv:2607. 17105v1 Announce Type: cross Abstract: It is crucial to safeguard computer networks from evolving network security threats and unknown cyberattacks.
arXiv Machine Learning
Aug 7
Behavioral Residualization for Unsupervised Intrusion Detection in Automotive CAN Networks
arXiv:2608. 05548v1 Announce Type: cross Abstract: Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message authentication or encryption.
arXiv Machine Learning
Jun 11
Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems
arXiv:2606. 12075v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks.
arXiv Machine Learning
Jun 16
Continual Backdoor Training in IoT/CPS
arXiv:2606. 14987v1 Announce Type: cross Abstract: Internet of Things (IoT) and Cyber-physical systems (CPS) increasingly rely on continual learning (CL) to adapt to evolving environments, device heterogeneity, and concept drift, thereby improving overall utility.
arXiv Machine Learning
Jul 30
Adaptively Robust LLM Monitoring via Activation Watermarking
arXiv:2603. 23171v3 Announce Type: replace-cross Abstract: Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent.