Machine learning in the field of information security
| dc.contributor.advisor | Νταντογιάν, Χριστόφορος | |
| dc.contributor.author | Kalachanis, Athanasios | |
| dc.date.accessioned | 2018-10-29T06:45:41Z | |
| dc.date.available | 2018-10-29T06:45:41Z | |
| dc.date.issued | 2018 | |
| dc.identifier.uri | https://dione.lib.unipi.gr/xmlui/handle/unipi/11485 | |
| dc.format.extent | 43 | el |
| dc.language.iso | en | el |
| dc.publisher | Πανεπιστήμιο Πειραιώς | el |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές | * |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές | * |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.title | Machine learning in the field of information security | el |
| dc.type | Master Thesis | el |
| dc.contributor.department | Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών Συστημάτων | el |
| dc.description.abstractEN | The purpose of this thesis is to analyze existing machine learning application in the information security field and demonstrate a machine learning malware classifier. At first, we’ll make a brief introduction into data science, malware, machine learning and adversarialmachine learning. Moreover, we will concentrate on applications of machine learning systems in the cyber security field and how an attacker can evade such systems and impact the integrity, availability and confidentialityby exploiting the classifiers vulnerabilities. Finally, we present a custom malware classifier as a proof of concept executablefiles. | el |
| dc.contributor.master | Ασφάλεια Ψηφιακών Συστημάτων | el |
| dc.subject.keyword | Machine learning | el |
| dc.subject.keyword | Information security | el |
| dc.subject.keyword | Malware | el |
| dc.date.defense | 2018-02-28 |
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Τμήμα Ψηφιακών Συστημάτων
Department of Digital Systems
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Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές