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Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

V. Praveena1, A. Vijayaraj2, P. Chinnasamy3, Ihsan Ali4,*, Roobaea Alroobaea5, Saleh Yahya Alyahyan6, Muhammad Ahsan Raza7

1 Department of Computer Science and Engineering, Dr. N. G. P Institute of Technology, Coimbatore, 641048, India
2 Department of Information Technology, Vignan’s Foundation for Science, Technology & Research, Guntur, 522213, India
3 Department of Information Technology, Sri Shakthi Institute of Engineering and Technology, Coimbatore, 641062, India
4 Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia
5 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
6 Department of Computer Science, Community College in Dwadmi, Shaqra University, 11961, Saudi Arabia
7 Department of Information Technology, Bahauddin Zakariya University, Multan, 60000, Pakistan

* Corresponding Author: Ihsan Ali. Email: email

Computers, Materials & Continua 2022, 70(2), 2639-2653.


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.


Cite This Article

APA Style
Praveena, V., Vijayaraj, A., Chinnasamy, P., Ali, I., Alroobaea, R. et al. (2022). Optimal deep reinforcement learning for intrusion detection in uavs. Computers, Materials & Continua, 70(2), 2639-2653.
Vancouver Style
Praveena V, Vijayaraj A, Chinnasamy P, Ali I, Alroobaea R, Alyahyan SY, et al. Optimal deep reinforcement learning for intrusion detection in uavs. Comput Mater Contin. 2022;70(2):2639-2653
IEEE Style
V. Praveena et al., "Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs," Comput. Mater. Contin., vol. 70, no. 2, pp. 2639-2653. 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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