EUVD-2012-1477

Malware in sbrugna...

EUVD-2020-17655

Malware in sbrugna...

EUVD-2012-1447

Malware in sbrugna...

EUVD-2012-1480

Malware in sbrugna...

EUVD-2010-1453

Malware in sbrugna...

EUVD-2012-1466

Malware in sbrugna...

EUVD-2012-1464

Malware in sbrugna...

EUVD-2012-1476

Malware in sbrugna...

EUVD-2008-5519

Malware in sbrugna...

EUVD-2017-3791

Malware in sbrugna...

EUVD-2008-5515

Malware in sbrugna...

EUVD-2008-5505

Malware in sbrugna...

EUVD-2012-1479

Malware in sbrugna...

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks

As deep learning models become widely deployed as components within larger production systems, their individual shortcomings can create system-level vulnerabilities with real-world impact. This paper studies how adversarial attacks targeting an ML component can degrade or bypass an entire...

Binary Diff Summarization Using Large Language Models

Security of software supply chains is necessary to ensure that software updates do not contain maliciously injected code or introduce vulnerabilities that may compromise the integrity of critical infrastructure. Verifying the integrity of software updates involves binary differential analysis...

TRUSTCHECKPOINTS: Time Betrays Malware for Unconditional Software Root of Trust

Modern IoT and embedded platforms must start execution from a known trusted state to thwart malware, ensure secure firmware updates, and protect critical infrastructure. Current approaches to establish a root of trust depend on secret keys and/or specialized secure hardware, which drives up costs...

"Digital Camouflage": the LLVM Challenge in LLM-Based Malware Detection

Large Language Models LLMs have emerged as promising tools for malware detection by analyzing code semantics, identifying vulnerabilities, and adapting to evolving threats. However, their reliability under adversarial compiler-level obfuscation is yet to be discovered. In this study, we empirical...

DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations

In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain...

Malicious code in palynology-galaxy-fusion-playwright (npm)

The package palynology-galaxy-fusion-playwright was found to contain malicious code...

Quantum AI Algorithm Development for Enhanced Cybersecurity: a Hybrid Approach to Malware Detection

This study explores the application of quantum machine learning QML algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealin...