Optimal deep reinforcement learning for intrusion detection in UAVs

Praveena, V. and Vijayaraj, A. and Chinnasamy, P. and Ali, Ihsan and Alroobaea, Roobaea and Alyahyan, Saleh Yahya and Raza, Muhammad Ahsan (2022) Optimal deep reinforcement learning for intrusion detection in UAVs. CMC-Computers Materials & Continua, 70 (2). pp. 2639-2653. ISSN 1546-2218, DOI https://doi.org/10.32604/cmc.2022.020066.

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In recent years, progressive developments have been observed in recent technologies and the production cost has been continuously decreasing. In such scenario, Internet of Things (IoT) network which is comprised of a set of Unmanned Aerial Vehicles (UAV), has received more attention from civilian to military applications. But network security poses a serious challenge to UAV networks whereas the intrusion detection system (IDS) is found to be an effective process to secure the UAV networks. Classical IDSs are not adequate to handle the latest computer networks that possess maximum bandwidth and data traffic. In order to improve the detection performance and reduce the false alarms generated by IDS, several researchers have employed Machine Learning (ML) and Deep Learning (DL) algorithms to address the intrusion detection problem. In this view, the current research article presents a deep reinforcement learning technique, optimized by Black Widow Optimization (DRL-BWO) algorithm, for UAV networks. In addition, DRL involves an improved reinforcement learning-based Deep Belief Network (DBN) for intrusion detection. For parameter optimization of DRL technique, BWO algorithm is applied. It helps in improving the intrusion detection performance of UAV networks. An extensive set of experimental analysis was performed to highlight the supremacy of the proposed model. From the simulation values, it is evident that the proposed method is appropriate as it attained high precision, recall, F-measure, and accuracy values such as 0.985, 0.993, 0.988, and 0.989 respectively.

Item Type: Article
Funders: Faculty of Computer Science and Infor-mation Technology, University of Malaya [Grant No: PG035-2016A]
Uncontrolled Keywords: Intrusion detection; UAV networks; Reinforcement learning; Deep learning; Parameter optimization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 27 Jul 2022 04:18
Last Modified: 27 Jul 2022 04:18
URI: http://eprints.um.edu.my/id/eprint/33584

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