The paper proposes a novel method to generate adversarial malware samples that evade deep learning detectors while simultaneously minimizing the detectable 'drift' signals, showing that similarity con…

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…

The paper analyzes existing hardware Trojan datasets to demonstrate that standard cell libraries can be systematically exploited to create visually undetectable, stealthy hardware Trojans, exemplified…

The paper demonstrates that current defenses against malicious fine-tuning of foundation models are insufficient because they only address fixed attacks, and introduces a unified adaptive attack that…

The paper proposes a framework to intentionally evade malware detectors by adding a small number of benign API imports, successfully demonstrating targeted misclassification into a chosen benign categ…

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…

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 Landseer, a modular framework designed to systematically evaluate and compose multiple machine learning defenses to address complex, real-world security requirements.

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 introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

The paper constructs a large, adversarial malware dataset from real-world binaries, demonstrating high evasion rates and showing that even small amounts of poisoned data can severely compromise malwar…

The paper introduces a novel stateful online monitoring system that detects distributed multi-agent cyberattacks by aggregating weak suspiciousness signals across many user accounts, overcoming the bl…

The paper introduces a novel stateful online monitoring system that detects distributed multi-agent cyberattacks by aggregating weak suspiciousness signals across many user accounts, overcoming the bl…

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.

The paper proposes DynaHug, a dynamic analysis technique that uses machine learning to detect malicious pre-trained machine learning models by learning the runtime behaviors of benign models, achievin…

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 identifies a critical vulnerability, the Camouflage Detection Gap (CDG), where standard LLM injection detectors fail dramatically when malicious payloads mimic the target domain's language a…

The paper introduces 'adversarial restlessness,' an activation-level signature in LLM residual streams, to detect multi-turn prompt injection attacks with high accuracy.

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…

ML Defender (aRGus NDR) is an open-source, embedded Machine Learning Network Intrusion Detection System (NIDS) that achieves superior detection rates for botnet and anomalous traffic on resource-const…