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AI-Driven Ensemble Classifier for Jamming Attack Detection in VANETs to Enhance Security in Smart Cities | IEEE Journals & Magazine | IEEE Xplore

AI-Driven Ensemble Classifier for Jamming Attack Detection in VANETs to Enhance Security in Smart Cities


Steps of the proposed jamming attack detection approach.

Abstract:

Vehicular Ad-hoc Networks (VANETs) are integral to the fabric of Intelligent Transportation Systems (ITSs), facilitating essential vehicle-to-vehicle (V2V) and vehicle-to...Show More

Abstract:

Vehicular Ad-hoc Networks (VANETs) are integral to the fabric of Intelligent Transportation Systems (ITSs), facilitating essential vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, the rising prevalence of jamming attacks, characterized by the intentional disruption of communications through interference signals, presents a significant challenge to the security of VANETs and, consequently, public safety. This emerging threat highlights a critical research gap in the development of sophisticated, AI-driven security solutions for VANETs. In response to this challenge, our study introduces an innovative artificial intelligence (AI) model, meticulously engineered to detect jamming attacks in VANETs. This model represents a synergistic integration of an array of machine learning (ML) and deep learning (DL) classifiers, meticulously analyzing signal characteristics within VANET communication channels. Its primary aim is the effective identification of anomalous patterns signaling the presence of jamming attacks. Extensive simulations were conducted to rigorously test the model’s efficacy, which yielded encouraging results. Initially, we assessed the detection accuracy of 14 different ML classifiers and 4 DL classifiers. Subsequently, we proposed a voting-based ensemble AI classifier combining the most accurate ML and DL classifiers, namely Random Forest (RF), Extra Tree (ET), and fine-tuned Convolutional Neural Network (CNN). This ensemble classifier, RF+ET+CNN, achieved the highest detection accuracy, outperforming the individual classifiers. Specifically, the CNN algorithm demonstrated an exceptional detection accuracy of 99.133%, while the RF and ET classifiers were the most accurate among the ML algorithms tested, with accuracy rates of 97.4359% and 97.4357%, respectively. Notably, the proposed RF+ET+CNN ensemble classifier achieved an impressive detection accuracy of 99.8125%. These findings underscore the superiority of our propos...
Steps of the proposed jamming attack detection approach.
Published in: IEEE Access ( Volume: 13)
Page(s): 50687 - 50713
Date of Publication: 18 March 2025
Electronic ISSN: 2169-3536

Funding Agency:

Author image of Walid El-Shafai
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
Walid El-Shafai (Senior Member, IEEE) was born in Alexandria, Egypt. He received the B.Sc. degree (Hons.) in electronics and electrical communication engineering from the Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, Egypt, in 2008, the M.Sc. degree from Egypt-Japan University of Science and Technology (E-JUST), in 2012, and the Ph.D. degree from FEE, Menoufia University, in 2019. From January 2021...Show More
Walid El-Shafai (Senior Member, IEEE) was born in Alexandria, Egypt. He received the B.Sc. degree (Hons.) in electronics and electrical communication engineering from the Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, Egypt, in 2008, the M.Sc. degree from Egypt-Japan University of Science and Technology (E-JUST), in 2012, and the Ph.D. degree from FEE, Menoufia University, in 2019. From January 2021...View more
Author image of Ahmad Taher Azar
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
Ahmad Taher Azar (Senior Member, IEEE) is a Full Professor with the College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia. He is a Leader of the Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University. He has expertise in artificial intelligence, control theory and applications, robotics, machine learning, computational intelligence, and dynamical sys...Show More
Ahmad Taher Azar (Senior Member, IEEE) is a Full Professor with the College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia. He is a Leader of the Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University. He has expertise in artificial intelligence, control theory and applications, robotics, machine learning, computational intelligence, and dynamical sys...View more
Author image of Saim Ahmed
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
Saim Ahmed received the B.Sc. degree in electronics from the Sir Syed University of Science and Technology, Pakistan, in 2009, the M.E. degree in industrial control and automation from Hamdard University, Pakistan, in 2013, and the Ph.D. degree in control science and engineering from Nanjing University of Science and Technology, China, in 2019. He is currently a Postdoctoral Researcher with the Department of Computer Scie...Show More
Saim Ahmed received the B.Sc. degree in electronics from the Sir Syed University of Science and Technology, Pakistan, in 2009, the M.E. degree in industrial control and automation from Hamdard University, Pakistan, in 2013, and the Ph.D. degree in control science and engineering from Nanjing University of Science and Technology, China, in 2019. He is currently a Postdoctoral Researcher with the Department of Computer Scie...View more

Author image of Walid El-Shafai
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt
Walid El-Shafai (Senior Member, IEEE) was born in Alexandria, Egypt. He received the B.Sc. degree (Hons.) in electronics and electrical communication engineering from the Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, Egypt, in 2008, the M.Sc. degree from Egypt-Japan University of Science and Technology (E-JUST), in 2012, and the Ph.D. degree from FEE, Menoufia University, in 2019. From January 2021 to December 2024, he was a Researcher with the Security Engineering Laboratory (SEL), Prince Sultan University (PSU), Riyadh, Saudi Arabia. He is currently a Senior Researcher with the Automated Systems and Soft Computing Laboratory (ASSCL); and an Assistant Professor with the College of Computer Science and Information Systems, PSU. In addition, he is an Associate Professor with the Department of Electronics and Communication Engineering (ECE), Faculty of Electronic Engineering, Menoufia University. His research interests include wireless mobile and multimedia communication systems, image and video signal processing, efficient 2-D/3-D video coding and transmission, quality of service and experience, digital communication techniques, cognitive radio networks, adaptive filter design, 3-D video watermarking, steganography, encryption, error resilience and concealment algorithms for video codecs (H.264/AVC, H.264/MVC, and H.265/HEVC), cognitive cryptography, medical image processing, speech processing, security algorithms, software-defined networks, the Internet of Things, FPGA implementations of signal processing algorithms and communication systems, cancellable biometrics, pattern recognition, image and video magnification, artificial intelligence applications in signal processing and communication systems, modulation identification and classification, image and video super-resolution and denoising, automated systems, cybersecurity applications, malware and ransomware detection, and deep learning for signal processing and communication systems. He is a dedicated reviewer of several international journals and conferences, contributing to the advancement of research in his areas of expertise.
Walid El-Shafai (Senior Member, IEEE) was born in Alexandria, Egypt. He received the B.Sc. degree (Hons.) in electronics and electrical communication engineering from the Faculty of Electronic Engineering (FEE), Menoufia University, Menouf, Egypt, in 2008, the M.Sc. degree from Egypt-Japan University of Science and Technology (E-JUST), in 2012, and the Ph.D. degree from FEE, Menoufia University, in 2019. From January 2021 to December 2024, he was a Researcher with the Security Engineering Laboratory (SEL), Prince Sultan University (PSU), Riyadh, Saudi Arabia. He is currently a Senior Researcher with the Automated Systems and Soft Computing Laboratory (ASSCL); and an Assistant Professor with the College of Computer Science and Information Systems, PSU. In addition, he is an Associate Professor with the Department of Electronics and Communication Engineering (ECE), Faculty of Electronic Engineering, Menoufia University. His research interests include wireless mobile and multimedia communication systems, image and video signal processing, efficient 2-D/3-D video coding and transmission, quality of service and experience, digital communication techniques, cognitive radio networks, adaptive filter design, 3-D video watermarking, steganography, encryption, error resilience and concealment algorithms for video codecs (H.264/AVC, H.264/MVC, and H.265/HEVC), cognitive cryptography, medical image processing, speech processing, security algorithms, software-defined networks, the Internet of Things, FPGA implementations of signal processing algorithms and communication systems, cancellable biometrics, pattern recognition, image and video magnification, artificial intelligence applications in signal processing and communication systems, modulation identification and classification, image and video super-resolution and denoising, automated systems, cybersecurity applications, malware and ransomware detection, and deep learning for signal processing and communication systems. He is a dedicated reviewer of several international journals and conferences, contributing to the advancement of research in his areas of expertise.View more
Author image of Ahmad Taher Azar
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
Ahmad Taher Azar (Senior Member, IEEE) is a Full Professor with the College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia. He is a Leader of the Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University. He has expertise in artificial intelligence, control theory and applications, robotics, machine learning, computational intelligence, and dynamical system modeling. He has authored/co-authored over 500 research articles in prestigious peer-reviewed journals, book chapters, and conference proceedings. He is currently an Editor of IEEE Systems Journal, IEEE Transactions on Neural Networks and Learning Systems, Human-Centric Computing and Information Sciences (Springer), and Engineering Applications of Artificial Intelligence (Elsevier).
Ahmad Taher Azar (Senior Member, IEEE) is a Full Professor with the College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia. He is a Leader of the Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University. He has expertise in artificial intelligence, control theory and applications, robotics, machine learning, computational intelligence, and dynamical system modeling. He has authored/co-authored over 500 research articles in prestigious peer-reviewed journals, book chapters, and conference proceedings. He is currently an Editor of IEEE Systems Journal, IEEE Transactions on Neural Networks and Learning Systems, Human-Centric Computing and Information Sciences (Springer), and Engineering Applications of Artificial Intelligence (Elsevier).View more
Author image of Saim Ahmed
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
Automated Systems and Soft Computing Laboratory (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia
Saim Ahmed received the B.Sc. degree in electronics from the Sir Syed University of Science and Technology, Pakistan, in 2009, the M.E. degree in industrial control and automation from Hamdard University, Pakistan, in 2013, and the Ph.D. degree in control science and engineering from Nanjing University of Science and Technology, China, in 2019. He is currently a Postdoctoral Researcher with the Department of Computer Science, Prince Sultan University, Riyadh, Saudi Arabia. His research interests include the theory and applications of adaptive control, sliding mode control, time delay control, robotic manipulators, and nonlinearities and their compensation.
Saim Ahmed received the B.Sc. degree in electronics from the Sir Syed University of Science and Technology, Pakistan, in 2009, the M.E. degree in industrial control and automation from Hamdard University, Pakistan, in 2013, and the Ph.D. degree in control science and engineering from Nanjing University of Science and Technology, China, in 2019. He is currently a Postdoctoral Researcher with the Department of Computer Science, Prince Sultan University, Riyadh, Saudi Arabia. His research interests include the theory and applications of adaptive control, sliding mode control, time delay control, robotic manipulators, and nonlinearities and their compensation.View more

References

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