Archimedes Optimization with Deep Learning Based Aerial Image Classification for Cybersecurity Enabled UAV Networks  

作　　者：Faris Kateb Mahmoud Ragab 

机构地区：[1]Information Technology Department,Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第11期2171-2185,共15页计算机系统科学与工程（英文）

基　　金：funded by Institutional Fund Projects under Grant No.(IFPIP:511-611-1443).

摘　　要：The recent adoption of satellite technologies,unmanned aerial vehicles(UAVs)and 5G has encouraged telecom networking to evolve into more stable service to remote areas and render higher quality.But,security concerns with drones were increasing as drone nodes have been striking targets for cyberattacks because of immensely weak inbuilt and growing poor security volumes.This study presents an Archimedes Optimization with Deep Learning based Aerial Image Classification and Intrusion Detection(AODL-AICID)technique in secure UAV networks.The presented AODLAICID technique concentrates on two major processes:image classification and intrusion detection.For aerial image classification,the AODL-AICID technique encompasses MobileNetv2 feature extraction,Archimedes Optimization Algorithm(AOA)based hyperparameter optimizer,and backpropagation neural network(BPNN)based classifier.In addition,the AODLAICID technique employs a stacked bi-directional long short-term memory(SBLSTM)model to accomplish intrusion detection for cybersecurity in UAV networks.At the final stage,the Nadam optimizer is utilized for parameter tuning of the SBLSTM approach.The experimental validation of the AODLAICID technique is tested and the obtained values reported the improved performance of the AODL-AICID technique over other models.

关 键 词：Aerial image classification remote sensing intrusion detection CYBERSECURITY deep learning 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TP393.08[自动化与计算机技术—计算机应用技术]