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Title Classification of Cyber attacks on IoT using Machine Learning

Author(s) Hamza Ishtiaq

Abstract Internet of Things (IoT) devices are growing rapidly, which are raising concerns regarding data security and privacy. With the increase in volume of data generated by IoT devices, secure transmission and processing has become essential. Protecting IoT infrastructure and devices from cyber-attacks is an active research area now-a-days. The detection and classification of attacks on IoT require dynamic and automatic techniques, based on Machine Learning (ML). This thesis focuses on the classification of cyber-attacks on IoT using ML. It explores the potential of ensemble learning to enhance the accuracy of ML. Two different publicly available datasets are used in this thesis. These datasets are UNSW NB 15 and NSL KDD. The ensemble learning is implemented by combining the strengths of Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN). The proposed ensemble learner is named as X-DNN. The performance evaluation of X-DNN is carried out using, accuracy, precision, recall, and F1 scores. The experimental results show that X-DNN demonstrates an accuracy of 85.36% in classifying cyber-attacks on UNSW NB 15 dataset whereas it achieves accuracy of 99.81% on NSL KDD dataset. X-DNN outperforms state of the art methods such as Hierarchical Clustering Decision Tree Twin Support Vector Machine, Gated Recurrent Unit, and Deep Neural Networks. The proposed method offers relatively better performance which highlights its significance in classifying cyber-security attacks on IoT.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2024-01-04

Subject Electrical Engineering

Publisher NUML

Contributor(s) Hamza Ishtiaq

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