The paper introduces C-MADF, a causally constrained multi-agent framework that significantly reduces false positives in autonomous cyber defense by restricting response actions to structurally consist…

The paper proposes a Digital Twin-enabled Simultaneous Learning and Modeling (DT-SLAM) framework to enhance secure communications in UAV-assisted networks against intelligent eavesdropping attacks, ac…

This paper reviews advanced AI-based solutions, specifically combining LLMs and RL, to create dynamic and cost-effective network and device-level cyber deception strategies for contested environments.

This paper proposes a cloud-edge-end collaborative defense framework to secure UAV swarms against various threats like GPS spoofing and multi-hop intrusions, demonstrating its effectiveness through ex…

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…

This paper proposes using a zero-determinant (ZD) strategy to construct an effective Moving Target Defense (MTD) that maintains performance comparable to the optimal Stackelberg equilibrium while dras…

The paper introduces a threat-oriented digital twinning methodology to enable reproducible and controllable cybersecurity evaluation of autonomous platforms, overcoming limitations in accessing real-w…

The paper introduces a queueing-theoretic framework to model dynamic cyber-attack surfaces, developing an adaptive reinforcement learning defense policy that significantly reduces active vulnerabiliti…

The paper proposes a bilevel optimization framework to model the adversarial co-evolution between malware attackers and detection models, achieving near-total immunity against sophisticated evasion at…

The paper proposes a tool-mediated LLM architecture for autonomous cyber defense, formally proving its stability and demonstrating that it significantly reduces an attacker's expected payoff in real-w…

The paper proposes an autonomous red teaming framework combining LLMs and RL to generate sophisticated, multi-stage cyber attack campaigns, demonstrating its necessity for evaluating robust AI-enabled…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these honeypots provide substantially longer and harder-to-detect…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these systems provide substantially longer and harder-to-detect i…

This paper demonstrates that using a communication algorithm (CommFormer) with heterogeneous agents significantly improves the speed and performance of multi-agent reinforcement learning for autonomou…

The paper proposes a system that operationalizes cybersecurity governance frameworks by integrating them with attack-path modeling and Deep Reinforcement Learning to generate practical, resource-const…

The paper proposes a Digital Twin-assisted Adaptive Multi-Agent Deep Reinforcement Learning framework to intelligently manage spectrum and resources in complex, dynamic Open-RAN 6G networks utilizing…

The paper demonstrates that soft fusion in multi-warden covert communication has structural limits, showing that the Fusion Center gains no significant detection advantage from randomizing the number…

This paper evaluates the physical transfer of adversarial patches against aerial vehicle detectors, finding that while digitally optimized patches can be highly effective, their real-world robustness…

This paper analyzes the latency-accuracy trade-offs of various TinyML models for detecting diverse cyber-RF threats on autonomous spacecraft, finding that Logistic Regression offers an effective, low-…

DeepXplain introduces an explainable deep reinforcement learning framework that enhances the trustworthiness and effectiveness of autonomous cyber defense against multi-stage APT campaigns by integrat…