Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

作     者：V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza 

作者机构：Department of Computer Science and EngineeringDr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information TechnologyVignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information TechnologySri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and TechnologyFaculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer ScienceCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer ScienceCommunity College in DwadmiShaqra University11961Saudi Arabia Department of Information TechnologyBahauddin Zakariya UniversityMultan60000Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第2期

页      面：2639-2653页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is also supported by the Faculty of Computer Science and Information Technology University of Malaya under Postgraduate Research Grant(PG035-2016A) 

主　　题：Intrusion detection UAV networks reinforcement learning deep learning parameter optimization 

摘      要：In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary *** 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 *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** 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 *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of UAV *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** 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.