191 citations · 334 across the 6 of their papers we have counts for
6 papers
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
Daniel Gibert, Giulio Zizzo, Quan Le
Malware detectors based on deep learning (DL) have been shown to be susceptible to malware examples that have been deliberately manipulated in order to evade detection, a.k.a. adve…
Query-Free Evasion Attacks Against Machine Learning-Based Malware Detectors with Generative Adversarial Networks
Daniel Gibert, Jordi Planes, Quan Le +1
Malware detectors based on machine learning (ML) have been shown to be susceptible to adversarial malware examples. However, current methods to generate adversarial malware example…
Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classification
Daniel Gibert, Carles Mateu, Jordi Planes +1
Machine learning has become an appealing signature-less approach to detect and classify malware because of its ability to generalize to never-before-seen samples and to handle larg…
Enhancing the Insertion of NOP Instructions to Obfuscate Malware via Deep Reinforcement Learning
Daniel Gibert, Matt Fredrikson, Carles Mateu +2
Current state-of-the-art research for tackling the problem of malware detection and classification is centered on the design, implementation and deployment of systems powered by ma…
Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events
Xiaoyu Du, Quan Le, Mark Scanlon
Case-hindering, multi-year digital forensic evidence backlogs have become commonplace in law enforcement agencies throughout the world. This is due to an ever-growing number of cas…
Deep learning at the shallow end: Malware classification for non-domain experts
Quan Le, Oisín Boydell, Brian Mac Namee +1
Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malwa…