Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables

12 Mar 2018Bojan KolosnjajiAmbra DemontisBattista BiggioDavide MaiorcaGiorgio GiacintoClaudia EckertFabio Roli

Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fields, instruction sequences, or even raw bytes, to learn models that discriminate between benign and malicious software... (read more)

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