Evaluating the Fidelity and Efficiency of Network Intrusion Detection Systems Via Deep Learning, Machine Learning, and Deep Hybrid Learning in Industrial IoT Devices

dc.contributor.advisorChen, F. Frank
dc.contributor.authorShahin, Mohammad
dc.contributor.committeeMemberXu, Kefeng
dc.contributor.committeeMemberCastillo, Krystel
dc.contributor.committeeMemberWan, Hung-Da
dc.date.accessioned2024-02-12T20:02:27Z
dc.date.available2024-02-12T20:02:27Z
dc.date.issued2022
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThe rise of Industry 4.0 has urged scholars to pursue the possibility of integrating these novel technologies with traditional manufacturing practices such as Lean and Six Sigma. However, the main obstacle in the way to achieving such a vision is the concerns arising from the increasing number of cybersecurity threats. Even though there is a sizable amount of research that has already been done on such integration, the cybersecurity concerns regarding that have been mainly overlooked. To this end, this dissertation proposes applying DL (Deep Learning), ML (Machine Learning), and DHL (Deep Hybrid Learning) to classify and detect such threats. First, novel DL algorithms were deployed on three benchmark Network Intrusion Detection Systems (NIDS) datasets to detect cybersecurity attacks. These datasets are known as UNSW-NB15, BoT-IoT, and ToN-IoT, the proposed DL models showed results with high accuracy values. Then a combination of DL and ML algorithms was utilized with a primary objective to propose highly secured and accurate methods for the successful identification of cybersecurity threats through botnet attacks on the N-BaIoT dataset, the results showed high-performance accuracy and proved the models to be a robust approach for threat detection. Finally, and to improve NIDS, new state-of-the-art DHL models were developed by combining ML and DL algorithms; and then deployed to detect anomalies in traffic data of Industrial IoT devices from the TON_IoT dataset. Results showed that the newly proposed DHL models can successfully detect a variety of cybersecurity attacks. In summary, the results substantiate the robustness of the deployed algorithms.
dc.description.departmentMechanical Engineering
dc.format.extent107 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9798438751250
dc.identifier.urihttps://hdl.handle.net/20.500.12588/5343
dc.languageen
dc.subjectAI
dc.subjectAnalytics
dc.subjectCybersecurity
dc.subjectDetection
dc.subjectIoT
dc.subjectInternet of things
dc.subject.classificationIndustrial engineering
dc.subject.classificationComputer engineering
dc.subject.classificationWeb studies
dc.subject.classificationArtificial intelligence
dc.titleEvaluating the Fidelity and Efficiency of Network Intrusion Detection Systems Via Deep Learning, Machine Learning, and Deep Hybrid Learning in Industrial IoT Devices
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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