Automatic Malware Signature Generation
Michele Crepaldi
Automatic Malware Signature Generation.
Rel. Antonio Lioy, Andrea Atzeni. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021
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Abstract
In most recent years the proliferation of malicious software, namely Malware, has had a massive increase: according to AV Atlas Dashboard the new malware samples (and PUA - Potentially Unwanted Application) currently detected every day are about 440.000 (at the time of writing), and this number is predicted to only keep growing. The total number of known Microsoft Windows malicious software (and PUA) passed from about 55 million in 2011, to about 400 million in 2016, and finally to nearly 830 million now. The huge number of malware samples out there in the wild renders the detection through manually generated signatures (patterns that identify malicious code) infeasible and consequently imposes the urgent need for tools able to automatically detect malware and possibly describe it in a human-interpretable way.
Several methodologies have been proposed through the years, ranging from signature-based detection (especially with Yara Rules) to various Machine Learning approaches like Decision Trees, Naive Bayes models and Neural Networks
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