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Web malware detection combining automatic code analysis, sequences mining and Machine Learning

Rebecca Pelaca'

Web malware detection combining automatic code analysis, sequences mining and Machine Learning.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021

Abstract:

Nowadays the use of both Web Pages and Applications is constantly growing and browser-based malicious infections are extensively widespread, getting the end users' devices more and more vulnerable. The main purpose of this master thesis research is to implement an almost completely automatic methodology to recognize JavaScript malware families and to assist analyst on knowing about this cyber-security domain. The model is tested on the 20% of the same malware family files with a k-fold, other malicious but different type file and a set of good samples. In this phase it expects the model recognizes the specific malware type from the rest, classifying good samples and other malware in the same way as outliers. The results on Cryxos are promising: the accuracy reaches 98%, with a precision of 100% on the inlier class.

Relatori: Paolo Garza
Anno accademico: 2020/21
Tipo di pubblicazione: Elettronica
Numero di pagine: 63
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-32 - INGEGNERIA INFORMATICA
Aziende collaboratrici: ERMES CYBER SECURITY S.R.L.
URI: http://webthesis.biblio.polito.it/id/eprint/18161
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