Abstract

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.

Highlights

  • The exponential developments in the fields of cloud computing and artificial intelligence technologies have drastically improved the design of Internet of Things (IoT) technologies

  • Due to the progression of technologies and constant reduction in production cost, there is an increasing penetration of IoT network that comprises of Unmanned Aerial Vehicles (UAVs) right starting from manufacturing & production to daily lives of the people in terms of border surveillance

  • The current research work presents a Deep Reinforcement Learning technique optimized by Black Widow Optimization (DRL-BWO) algorithm for UAV networks

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Summary

Introduction

The exponential developments in the fields of cloud computing and artificial intelligence technologies have drastically improved the design of Internet of Things (IoT) technologies. Since the attacks are highly complicated, automatic, and distributed, the classical IDS does not fulfil the requirements of recent network security challenges This scenario enhances the Detection Rate (DR) and diminish the false alarm frequency of IDSs. Various studies have presented Machine Learning (ML) techniques in the domain of intrusion detection. The exploration level provides a chance to observe different & significant regions in a search space and generate new solutions to escape from the local optima issue. Spiders without fitness are discarded from the region which results in earlier convergence It significantly varies from other population-based optimization algorithms. The current research work presents a Deep Reinforcement Learning technique optimized by Black Widow Optimization (DRL-BWO) algorithm for UAV networks. For parameter optimization of DRL technique, BWO algorithm is applied which helps in improving the intrusion detection performance among UAV networks.

Related Works
The Proposed DRL-BWO Based Intrusion Detection in UAV Networks
Reinforcement Learning
DRL Based DBN Model
Parameter Optimization Process
Experimental Validation
Methods
Findings
Conclusion
Full Text
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