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Showing 1 to 2 of 2 for “"Adversarial Malware"”.
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MDEA : malware detection with evolutionary adversarial learning
… have used machine learning as a tool to detect malware. These applications take in raw or processed binary data to feed neural network models to classify benign or malicious files. Even though this approach has proved effective against dynamic changes, such as encrypting, obfuscating and packing …
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NeuroYara: Learning to Rank for Yara Rules Generation through Deep Language Modeling & Discriminative N-gram Encoding
Signature-based malware detection methods are simple, explainable, and efficient. One of the most ubiquitous tools is Yara. It is a widely-used syntax for writing malware signatures. Compared to machine learning models, Yara rules have a lower false-positive rate and better maintainability of the …