Securing Wi-Fi Networks: A Study on RF  Fingerprinting and CNN-Based Intrusion Detection

Hasan, K. M. Sazid; Sakib, Mohammed Shadman; Hassan, Shahriar

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dc.contributor.author	Hasan, K. M. Sazid
dc.contributor.author	Sakib, Mohammed Shadman
dc.contributor.author	Hassan, Shahriar
dc.date.accessioned	2024-01-16T06:06:02Z
dc.date.available	2024-01-16T06:06:02Z
dc.date.issued	2023-05-30
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Hu, ‘CNN-based Intrusion Classification for IEEE 802.11 Wireless Networks’, 2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020, pp. 830–833, Dec. 2020, doi: 10.1109/ICCC51575.2020.9345293. [47] R. Vishwakarma and R. Vennelakanti, ‘CNN Model Tuning for Global Road Damage Detection’, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, pp. 5609–5615, Dec. 2020, doi: 10.1109/BIGDATA50022.2020.9377902	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/2026
dc.description	Supervised by Prof. Dr. Khondokar Habibul Kabir, Department of Electrical and Electronics Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.description.abstract	Advancements in wireless communication technology not only enhanced seamless connectivity and information exchange but also instigated the intrusion in RF networks, a pivotal challenge to the security of wireless communication networks. This thesis introduces a paradigm shift in wireless communication systems, to prevent unauthorized access attempts and malicious activities by classifying radio signals using Convolutional Neural Networks (CNNs). Diverging from conventional methods, an end-to-end deep learning model is proposed, capable of direct learning from raw time-domain signals, thereby preventing the requirement for manual feature engineering. The model is designed to extract and utilize rich, hierarchical feature representations from various radio signal types with diverse modulation techniques. Tested against a comprehensive radio signal dataset, the model demonstrates significantly enhanced classification accuracy and impressive generalization capabilities with unseen signals. The study explores the influence of different optimization algorithms on model performance, revealing how strategic parameter tuning can improve computational efficiency without compromising classification accuracy. The research not only advances the use of deep learning in radio signal classification, but also lays a foundation for future studies examining CNNs' noise resilience, interference handling, and adaptability to more intricate signals, thereby fostering the evolution of intelligent, autonomous systems in signal processing.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Securing Wi-Fi Networks: A Study on RF Fingerprinting and CNN-Based Intrusion Detection	en_US
dc.type	Thesis	en_US