Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

R Gopi,Mahantesh Mathapati,Anwer Mustafa Hilal,B Prasad,Fahd N Al-Wesabi,Manal Abdullah Alohali,Sultan Ahmad

doi:10.32604/cmc.2022.023306

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.

Highlights

Vehicular Ad hoc Network (VANET) is one of the advanced and established networks and is named so, since the location of the vehicle alters at all instances of time
intelligent DoS attack detection with Congestion Control (IDoS-CC) technique includes Teaching and Learning Based Optimization (TLBO) algorithm to optimize the routes taken by vehicles via traffic signals and minimize the congestion instantaneously on a particular route which in turn to assure minimal fuel utilization
A new IDoS-CC approach is designed for VANET

Summary

Introduction

Vehicular Ad hoc Network (VANET) is one of the advanced and established networks and is named so, since the location of the vehicle alters at all instances of time. Beacon losses tend to occur at the time of standard operations of IEEE 802.11p MAC protocol with arbitrary access requirements This could be featured as the damage to wireless networks (viz., beacon transmission overlaps the results from various vehicles that could lead to congestion or collision). Erskine et al [12] used VFC incorporation with hybrid OA and SI algorithm including CSA, ABC, and GA to provide a real-time recognition of DoS attacks in IEEE 802.11p In this method, VFC was used for achieving a secured smart vehicular network. In Selvi et al [17], an ASPBT algorithm was presented as an efficient and reliable emergency message communication technique This protocol vigorously changes the amount of partitions and the periodicity of beacons to reduce the amount of recommunication. There are two major levels in TLBO approach i.e., ‘Learner Phase’ (learns through their communication) and ‘Teacher Phase’ (learns from teacher)

Teacher Phase

Design of GRU-DoSD Technique

Performance Validation

Conclusion