IoT Security Using Machine Learning Methods

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dc.contributor.authorHosseini Goki, Seyedamiryousef
dc.contributor.supervisorWu, Kui
dc.date.accessioned2023-04-17T18:11:48Z
dc.date.available2023-04-17T18:11:48Z
dc.date.copyright2023en_US
dc.date.issued2023-04-17
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe rapid growth of internet-connected devices has made robust cybersecurity measures essential to protect against cyber threats. IoT cybersecurity includes various methods and technologies to secure internet-connected devices and systems from cyber attacks. The unique nature of IoT devices and systems poses several challenges to cybersecurity, including limited processing power, minimal security features, and vulnerability to attacks like DoS and DDoS. Cybersecurity strategies for IoT include encryption, authentication, access control, and threat detection and response, which utilize machine learning and artificial intelligence technologies to identify and respond to potential cyber attacks in real-time. The report discusses two projects related to cybersecurity in IoT environments, one focused on developing an intrusion detection system (IDS) based on deep learning algorithms to detect DDoS attacks, and another focused on identifying potential abnormalities in IoT networks using a fingerprint. These projects highlight the importance of prioritizing cybersecurity measures to protect against the growing number of cyber threats facing IoT devices and systems.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationEsmaeili M, Goki SH, Masjidi BH, Sameh M, Gharagozlou H, Mohammed AS. ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD. Wireless Communications and Mobile Computing. 2022 Aug 21;2022.en_US
dc.identifier.urihttp://hdl.handle.net/1828/14954
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectIoTen_US
dc.subjectSecurityen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectMachine Learningen_US
dc.subjectDDoS Attacksen_US
dc.subjectFingerprinten_US
dc.titleIoT Security Using Machine Learning Methodsen_US
dc.typeprojecten_US

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