Show simple item record

dc.contributor.authorFerrucci, Richard
dc.contributor.authorKholidy, Hisham A.; Advisor
dc.date.accessioned2020-12-21T20:22:24Z
dc.date.available2020-12-21T20:22:24Z
dc.date.issued2020-05
dc.identifier.citationFerrucci, R., & Kholidy, H. A. (2020, May). A Wireless Intrusion Detection for the Next Generation (5G) Networks: A Master’s Thesis Presented to the Department of Network and Computer Security in Partial Fulfillment of the Requirements for the Master of Science Degree. College of Engineering, SUNY Polytechnic Institute.en_US
dc.identifier.urihttp://hdl.handle.net/1951/71329
dc.description.abstract5G data systems are closed to delivery to the public. The question remains how security will impact the release of this cutting edge architecture. 5G data systems will be sending massive amounts of personal data due to the fact that everybody in the world is using mobile phones these days. With everyone using a 5G device, this architecture will have a huge surface area for attackers to compromise. Using machine learning techniques previously applied to 802.11 networks. We will show that improving upon these previous works, we can have a better handle on security when it comes to 5G architecture security. We find that using a machine learning classifier known as LogIT boost, combined with a selected combination of feature selection, we can provide optimal results in identifying three different classes of traffic referred to as normal, flooding, and injection traffic. We drastically decrease the time taken to perform this classification while improving the results. We simulate the Device2Device (D2D) connections involved in the 5G systems using the AWID dataset. The evaluation and validation of the classification approach are discussed in details in this thesis.en_US
dc.publisherSUNY Polytechnic Instituteen_US
dc.subject5G systemsen_US
dc.subjectcybersecurityen_US
dc.subjectintrusion detectionen_US
dc.subjectmachine learningen_US
dc.subjectDevice2Deviceen_US
dc.subjectLogIT boosten_US
dc.subjectwireless systemsen_US
dc.titleA Wireless Intrusion Detection for the Next Generation (5G) Networksen_US
dc.title.alternativeA Master’s Thesis Presented to the Department of Network and Computer Security in Partial Fulfillment of the Requirements for the Master of Science Degreeen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record