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Found 5 papers, 2 papers with code
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing
Our findings reveal that the chunk-based smoothing classifiers exhibit greater resilience against adversarial malware examples generated with state-of-the-are evasion attacks, outperforming a non-smoothed classifier and a randomized smoothing-based classifier by a great margin.
American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge.
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
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.
Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events
This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case.
Deep learning at the shallow end: Malware classification for non-domain experts
Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification.
Ranked #1 on
Malware Classification
on Microsoft Malware Classification Challenge
(F1 score (5-fold) metric)
