Hybrid arithmetic optimization algorithm with deep learning model for secure Unmanned Aerial Vehicle networks

Sultanah M. Alshammari; Nofe A. Alganmi; Mohammed H. Ba-Aoum; Sami Saeed Binyamin; Abdullah AL-Malaise AL-Ghamdi; Mahmoud Ragab; Sultanah M. Alshammari; Nofe A. Alganmi; Mohammed H. Ba-Aoum; Sami Saeed Binyamin; Abdullah AL-Malaise AL-Ghamdi; Mahmoud Ragab

doi:10.3934/math.2024348

AIMS Mathematics

2024, Volume 9, Issue 3: 7131-7151. doi: 10.3934/math.2024348

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Hybrid arithmetic optimization algorithm with deep learning model for secure Unmanned Aerial Vehicle networks

1.
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2.
Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
3.
Industrial and Systems Engineering Department, Virginia Polytechnic Institute and State University, Blacksburg 24061, VA, USA
4.
Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
5.
Computer and Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
6.
Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
7.
Information Systems Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi Arabia
8.
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Received: 01 December 2023 Revised: 05 January 2024 Accepted: 18 January 2024 Published: 19 February 2024
MSC : 68M11, 68M25, 68T07, 68W1

Securing Unmanned Aerial Vehicle (UAV) systems is vital to safeguard the processes involved in operating the drones. This involves the execution of robust communication encryption processes to defend the data exchanged between the UAVs and ground control stations. Intrusion detection, powered by Deep Learning (DL) techniques such as Convolutional Neural Networks (CNN), allows the classification and identification of potential attacks or illegal objects in the operational region of the drone, thus distinguishing them from the routine basics. The current research work offers a new Hybrid Arithmetic Optimizer Algorithm with DL method for Secure Unmanned Aerial Vehicle Network (HAOADL-UAVN) model. The purpose of the proposed HAOADL-UAVN technique is to secure the communication that occurs in UAV networks via threat detection. At the primary level, the network data is normalized through min-max normalization approach in order to scale the input dataset into a useful format. The HAOA is used to select a set of optimal features. Next, the security is attained via Deep Belief Network Autoencoder (DBN-AE)-based threat detection. At last, the hyperparameter choice of the DBN-AE method is implemented using the Seagull Optimization Algorithm (SOA). A huge array of simulations was conducted using the benchmark datasets to demonstrate the improved performance of the proposed HAOADL-UAVN algorithm. The comprehensive results underline the supremacy of the HAOADL-UAVN methodology under distinct evaluation metrics.
- Unmanned Aerial Vehicle,
- arithmetic optimization algorithm,
- feature selection,
- autoencoder,
- deep learning
Citation: Sultanah M. Alshammari, Nofe A. Alganmi, Mohammed H. Ba-Aoum, Sami Saeed Binyamin, Abdullah AL-Malaise AL-Ghamdi, Mahmoud Ragab. Hybrid arithmetic optimization algorithm with deep learning model for secure Unmanned Aerial Vehicle networks[J]. AIMS Mathematics, 2024, 9(3): 7131-7151. doi: 10.3934/math.2024348

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Abstract

Securing Unmanned Aerial Vehicle (UAV) systems is vital to safeguard the processes involved in operating the drones. This involves the execution of robust communication encryption processes to defend the data exchanged between the UAVs and ground control stations. Intrusion detection, powered by Deep Learning (DL) techniques such as Convolutional Neural Networks (CNN), allows the classification and identification of potential attacks or illegal objects in the operational region of the drone, thus distinguishing them from the routine basics. The current research work offers a new Hybrid Arithmetic Optimizer Algorithm with DL method for Secure Unmanned Aerial Vehicle Network (HAOADL-UAVN) model. The purpose of the proposed HAOADL-UAVN technique is to secure the communication that occurs in UAV networks via threat detection. At the primary level, the network data is normalized through min-max normalization approach in order to scale the input dataset into a useful format. The HAOA is used to select a set of optimal features. Next, the security is attained via Deep Belief Network Autoencoder (DBN-AE)-based threat detection. At last, the hyperparameter choice of the DBN-AE method is implemented using the Seagull Optimization Algorithm (SOA). A huge array of simulations was conducted using the benchmark datasets to demonstrate the improved performance of the proposed HAOADL-UAVN algorithm. The comprehensive results underline the supremacy of the HAOADL-UAVN methodology under distinct evaluation metrics.

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