Applying Machine Learning to Cyber Security

Stomeo, Carlo (2018) Applying Machine Learning to Cyber Security. [Laurea magistrale], Università di Bologna, Corso di Studio in Informatica [LM-DM270]
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Abstract

Intrusion Detection Systems (IDS) nowadays are a very important part of a system. In the last years many methods have been proposed to implement this kind of security measure against cyber attacks, including Machine Learning and Data Mining based. In this work we discuss in details the family of anomaly based IDSs, which are able to detect never seen attacks, paying particular attention to adherence to the FAIR principles. This principles include the Accessibility and the Reusability of software. Moreover, as the purpose of this work is the assessment of what is going on in the state of the art we have selected three approaches, according to their reproducibility and we have compared their performances with a common experimental setting. Lastly real world use case has been analyzed, resulting in the proposal of an usupervised ML model for pre-processing and analyzing web server logs. The proposed solution uses clustering and outlier detection techniques to detect attacks in an unsupervised way.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Stomeo, Carlo
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
Curriculum C: Sistemi e reti
Ordinamento Cds
DM270
Parole chiave
IDS,Intrusio Detection System,Machine Learning,Outlier,Clustering,Unsupervised,Web Server Logs
Data di discussione della Tesi
20 Dicembre 2018
URI

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