The paper proposes a privacy-aware machine unlearning framework using SISA training to efficiently remove the influence of specific training data from RL-based ransomware detectors with minimal perfor…

The paper proposes an explainable attention-based LSTM framework to achieve early and reliable detection of advanced, AI-assisted ransomware by analyzing file system behavioral sequences.

RansomTrack introduces a hybrid behavioral analysis framework that combines static and dynamic feature extraction to achieve high-accuracy, low-latency, and explainable real-time ransomware detection.

By analyzing over 27,000 posts from 325 public ransomware leak sites, this paper demonstrates that ransomware groups exhibit non-random, predictable operational regularities concerning victim concentr…

The paper proposes a universal robustification framework to enhance drift-adaptive malware detectors against combined concept drift and adversarial attacks, significantly reducing attack success rates…

The paper proposes a cost-aware, adaptive maintenance framework using Reinforcement Learning (RL) and self-supervised learning to mitigate performance degradation (concept drift) in Android malware de…

This paper addresses the lack of research on adversarial malware generation for Linux ELF binaries by developing a new semantic-preserving generator that achieves a high evasion rate against modern de…

The paper introduces a reinforcement learning framework, inspired by AlphaZero, to automate and improve the proof search process within the Tamarin protocol analysis tool, resulting in shorter and mor…

The paper introduces CSTM-Bench, a comprehensive benchmark and evaluation framework demonstrating that standard session-bound AI guardrails fail against sophisticated, cross-session attacks that accum…

The paper systematically maps LLM agent vulnerabilities by testing 10,000 prompt variations, finding that 'goal reframing' language is the primary trigger for exploitation, rather than broad adversari…

The paper introduces Trident, a novel malware detection system that combines static features, LLM-derived behavioral rules, and direct LLM analysis to achieve superior robustness against concept drift…

This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept dri…

eDySec introduces a deep learning framework for dynamic behavioral analysis that significantly improves the detection of malicious software packages in the PyPI ecosystem by enhancing stability and ex…

This paper proposes a hybrid feature fusion and voting-based approach for automated malware detection and classification into specific malware families, achieving high performance metrics like an AUC…

The paper introduces VIPER, a novel backdoor attack framework that exploits the functional fusion of malicious and benign logic within dynamic prompt architectures, demonstrating a new, high-risk thre…

The paper introduces TrojanMerge, a framework demonstrating that model merging can be exploited to systematically compromise the safety alignment of multiple individually safe LLMs.

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 demonstrates that static malware classifiers often rely on superficial artifacts like packing and metadata rather than true malicious semantics, using the TRUSTEE interpretability tool to di…

The paper proposes a unified closed-loop threat taxonomy to systematically analyze and defend foundation models by explicitly framing the bidirectional security interactions between data and models.