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Please use this identifier to cite or link to this item: http://hdl.handle.net/10791/273

Title: Identifying Malicious VoIP Usage using Computational Intelligence
Authors: McKellar, Jason
Supervisor(s): Abaza, Mahmoud (Faculty of Science and Technology, School of Computing and Information Systems)
Examining Committee: Tan, Ching (Faculty of Science and Technology, School of Computing and Information Systems)
Bagheri, Ebrahim (Faculty of Science and Technology, School of Computing and Information Systems)
Degree: Master of Science, Information Systems (MScIS)
Department: Faculty of Science and Technology
Keywords: Machine Learning
Issue Date: 31-Oct-2018
Abstract: VoIP user accounts are a prime target for hackers to compromise for profit. VoIP accounts are targets of the same types of attacks as any other Internet account that is authorized with a username and password. Unlike many other Internet accounts VoIP has a direct monetary cost to the user being compromised. Toll-fraud perpetrated using a compro- mised VoIP account can accrue expensive toll-charges that either the user or the service provider are liable to pay for. This paper discusses the prior research in detecting unau- thorized usage on VoIP accounts. The researched methods are based on machine learning techniques. A new technique of using a Recurrent Neural Network for detecting unau- thorized usage periods on a VoIP account is developed and demonstrated. The technique uses a Long-Short Term Memory style of Recurrent Neural Network to achieve over a 99% accuracy when testing against calls tagged as occurring during a toll-fraud event.
Graduation Date: 22-Oct-2018
URI: http://hdl.handle.net/10791/273
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