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Machine learning for detecting ransomware attacks using BGP routing records

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2022-08-24
Authors/Contributors
Abstract
Analyzing and detecting Border Gateway Protocol (BGP) anomalies caused by evolving ransomware cyber attacks are topics of great interest in cyber security. Various anomaly detection approaches such as time series and historical-based analysis, statistical validation, reachability checks, and supervised machine learning have been applied to collected datasets. Supervised and semi-supervised machine learning techniques rely on label and unlabeled data that contain regular and anomalous events. They are publicly available from BGP collection sites such as Réseaux IP Européens (RIPE) and Route Views.
Document
Extent
120 pages.
Identifier
etd22152
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Trajkovic, Ljiljana
Language
English
Member of collection
Download file Size
etd22152.pdf 6.94 MB

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