Open Access

ARTICLE

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

R. Gopi¹, Mahantesh Mathapati², B. Prasad³, Sultan Ahmad⁴, Fahd N. Al-Wesabi⁵, Manal Abdullah Alohali^6,*, Anwer Mustafa Hilal⁷

1 Department of Computer Science and Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur, 621 212, India
2 Department of Computer Science and Engineering, RajaRajeswari College of Engineering, Bengaluru, 560074, India
3 Department of Information Technology, Vignan's Institute of Information Technology, Visakhapatnam, 530049, India
4 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, 11942, Saudi Arabia
5 Department of Computer Science, College of Science & Arts at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia & Faculty of Computer and IT, Sana'a University, Sana'a, 15347, Yemen
6 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11564, Saudi Arabia
7 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia

* Corresponding Author: Manal Abdullah Alohali. Email:

Computers, Materials & Continua 2022, 72(1), 141-156. https://doi.org/10.32604/cmc.2022.023306

Received 02 September 2021; Accepted 01 November 2021; Issue published 24 February 2022

Abstract

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

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