| dc.contributor.advisor | Νταντογιάν, Χριστόφορος | |
| dc.contributor.author | Τσιαμπά, Παρασκευή | |
| dc.contributor.author | Tsiampa, Paraskevi | |
| dc.date.accessioned | 2018-02-27T10:58:47Z | |
| dc.date.available | 2018-02-27T10:58:47Z | |
| dc.date.issued | 2017 | |
| dc.identifier.uri | https://dione.lib.unipi.gr/xmlui/handle/unipi/11004 | |
| dc.format.extent | 41 | el |
| dc.language.iso | en | el |
| dc.publisher | Πανεπιστήμιο Πειραιώς | el |
| dc.title | Classification of exploit kits | el |
| dc.type | Master Thesis | el |
| dc.contributor.department | Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών Συστημάτων | el |
| dc.description.abstractEN | In this Thesis we examine all possible solutions presented by researchers across the world in the field of threat intelligence and more specifically in the direction of identifying exploit kits in the field. For every solution presented there is a brief report among with our point of view for each one. Moreover, we proceed with the implementation of a deep learning algorithm in Windows 10 environment, using Anaconda IDE. The goal is to create an algorithm able to identify pcap files that include exploit kit activity. We present our results and we conclude with future works goals. | el |
| dc.contributor.master | Τεχνοοικονομική Διοίκηση και Ασφάλεια Ψηφιακών Συστημάτων | el |
| dc.subject.keyword | Exploit Kits | el |
| dc.subject.keyword | Machine learning | el |
| dc.subject.keyword | Deep learning | el |
| dc.date.defense | 2018-02-16 | |
