{"id":{"repo_id":"athabasca","oai_identifier":"oai:dt.athabascau.ca:10791/273"},"canonical_url":"https://search.dev.ndltd.org/etd/athabasca/oai:dt.athabascau.ca:10791/273","repository":{"repo_id":"athabasca","name":"Athabasca University","base_url":"https://dt.athabascau.ca/oai/request"},"display":{"title":"Identifying Malicious VoIP Usage using Computational Intelligence","abstract":"2018-10-22","abstract_html":"2018-10-22","abstract_has_math":false,"creators":["McKellar, Jason"],"institution":"Athabasca University","degree_name":"Master of Science, Information Systems (MScIS)","degree_level":"master's","degree_discipline":"Faculty of Science and Technology","degree_department":null,"school":null,"contributors":["Abaza, Mahmoud (Faculty of Science and Technology, School of Computing and Information Systems)","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)"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-10-31","date_published":"2018-10-31","updated_at":"2026-08-21T16:41:56Z","subjects":["Machine Learning","VoIP"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["TC-AEAU-273"],"render_values":[{"text":"TC-AEAU-273","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10791/273","outbound_label":"Handle","outbound_source":"dc:identifier"},"source_record":{"url":"https://dt.athabascau.ca/oai/request?verb=GetRecord&metadataPrefix=oai_etdms&identifier=oai%3Adt.athabascau.ca%3A10791%2F273","prefix":"oai_etdms"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abaza, Mahmoud (Faculty of Science and Technology, School of Computing and Information Systems)","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)"]},{"key":"dc:creator","label":"Author","values":["McKellar, Jason"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-10-31"]},{"key":"dc:publisher","label":"Institution","values":["Athabasca University"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Science and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science, Information Systems (MScIS)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Athabasca University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","VoIP"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10791/273","https://dt.athabascau.ca/jspui/bitstream/10791/273/7/Thesis.pdf","TC-AEAU-273"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["2018-10-22","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."]},{"key":"dc:title","label":"Title","values":["Identifying Malicious VoIP Usage using Computational Intelligence"]}]}],"canonical_facts":{"dc:contributor":["Abaza, Mahmoud (Faculty of Science and Technology, School of Computing and Information Systems)","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)"],"dc:creator":["McKellar, Jason"],"dc:date":["2018-10-31"],"dc:description":["2018-10-22","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."],"dc:identifier":["http://hdl.handle.net/10791/273","https://dt.athabascau.ca/jspui/bitstream/10791/273/7/Thesis.pdf","TC-AEAU-273"],"dc:publisher":["Athabasca University"],"dc:subject":["Machine Learning","VoIP"],"dc:title":["Identifying Malicious VoIP Usage using Computational Intelligence"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Science and Technology"],"thesis:degree_level":["master's"],"thesis:degree_name":["Master of Science, Information Systems (MScIS)"],"thesis:institution_name":["Athabasca University"]},"updated_at":"2026-08-21T16:41:56Z"}