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Identifier 000460705
Title Fingerprinting the shadows: unmasking malicious servers with machine learning-powered TLS analysis
Alternative Title Αποτυπώνοντας τις σκιές: αποκάλυψη κακόβουλων διακομιστών μέσω ανάλυσης του πρωτοκόλλου TLS με την βοήθεια της μηχανικής μάθησης
Author Θεοφάνους, Ανδρέας Κ.
Thesis advisor Ιωαννίδης, Σωτήρης
Reviewer Δημητρόπουλος, Ξενοφώντας
Πρατικάκης, Πολύβιος
Abstract Over the last few years, the adoption of encryption in network traffic has been constantly increasing. The percentage of encrypted communications worldwide is estimated to exceed 90%. Although network encryption protocols mainly aim to secure and protect users online activities and communications, they have been exploited by malicious entities that hide their presence in the network. It was estimated that in 2022, more than 85% of the malware used encrypted communication channels. In this work, we examine state-of-the-art fingerprinting techniques and extend a machine learning pipeline for elective and practical server classification. Specifically, we actively contact servers to initiate communication over the TLS protocol and through exhaustive requests, we extract communication metadata. We investigate which features favor an effective classification, while we utilize and evaluate state-of-the-art approaches. Our extended pipeline can indicate whether a server is malicious or not with 91% precision and 95% recall, while it can specify the botnet family with 99% precision and 99% recall.
Language English
Subject Active probing
Botnet
Command and control
Explainability
Server characterization
Δακτυλικό αποτύπωμα
Διοίκηση και έλεγχος
Ενεργή ανίχνευση
Εξηγησιμότητα
Χαρακτηρισμός διακομιστή
Issue date 2023-12-01
Collection   School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/8/a/6/metadata-dlib-1701168849-461303-6179.tkl Bookmark and Share
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