Modified Marine Predators Algorithm With Deep Learning-Driven Security Solution for IoT-Assisted UAV Networks

• Published in: IEEE access Vol. 12; pp. 54991 - 54998
• Main Authors: Babu, S. Anantha, Ranganath, Abadhan, Goswami, Manish M., Gnanaprakasam, T., Ishak, Mohamad Khairi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; Classification algorithms; Cybersecurity; Deep learning; Entropy; Internet of Things; Intrusion detection; Intrusion detection system; Intrusion detection systems; Line of sight communication; Machine learning; Mathematical models; Neural networks; Predators; Security; Unmanned aerial vehicles; Vectors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3386570

Unmanned Aerial Vehicles (UAVs) are advanced technologies that are initially utilized for military apps like border monitoring and reconnaissance in opposed territories. Internet of Things (IoTs) assisted UAV networks suggest the combination of IoT technology with UAVs to generate a networked system that improves the abilities and utility of UAVs for several apps. UAVs' inherent features namely quick deployment, high dynamicity, low deployment and operational costs, and line of sight communication motivated researchers in the IoT field to assume UAV's combination into IoT systems near the concept of UAV-assisted IoT systems. However, security concerns with UAVs are evolving as UAV nodes are suitable attractive targets for cyber threats because of extremely developing volumes and poor and weak inbuilt security. Therefore, this paper presents a Modified Marine Predators Algorithm with a Deep Learning-Driven intrusion detection (MMPADL-ID) approach for IoT Assisted UAV Networks. The presented MMPADL-ID technique proposes to identify and classify the presence of intrusions in accomplishing security in IoT-assisted UAV networks. In the MMPADL-ID technique, the feature selection process is performed by the design of MMPA. In addition, the MMPADL-ID technique incorporates the Elman neural network (ENN) model for the recognition and classification of the intrusions. Furthermore, the honey badger algorithm (HBA) can be applied for the hyperparameter tuning of the ENN model and results in improved performance. The simulation value of the MMPADL-ID technique can be tested on benchmark datasets. An extensive comparative outcome reported the better solution of the MMPADL-ID algorithm with existing approaches for various aspects.