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dc.contributor.authorGUAN, YUE
dc.date.accessioned2023-06-27T13:23:13Z
dc.date.available2023-06-27T13:23:13Z
dc.identifier.urihttp://hdl.handle.net/10464/17884
dc.description.abstractThis work proposes an Anomaly Classification System for IoT (ACS-IoT). The proposed system contains a pipeline of machine learning and deep learning algorithms for the effective classification of anomalies and their sub-types. Machine learning algorithms are adopted to distinguish between normal data and anomaly data. The deep networks, on the other hand, are used to perform anomaly-type classification. We propose the use of the Synthetic Minority Oversampling Technique (SMOTE) to address the data imbalance problem and Particle Swarm Optimization (PSO) as a feature selection mechanism to improve accuracy as well as execution time. The proposed system proved to be accurate as well as precise when tested on a publicly available IoT dataset.en_US
dc.subjectIoT, Network Attacks, Deep Network, Intrusion Detection, Anomaly Classificationen_US
dc.titleACS-IoT: A CNN-BiLSTM Model for Anomaly Classification in IoT Networksen_US
refterms.dateFOA2023-06-24T00:00:00Z


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