Improved IDS for Vehicular Ad-Hoc Network using Deep Learning Approaches

In the contemporary landscape of transportation, Vehicular Adhoc networks (VANETs) have emerged as a pivotal infrastructure, facilitating real-time communication among vehicles and infrastructure to enhance road safety and transportation efficiency. However, the dynamic and open nature of VANETs exposes them to diverse security threats, necessitating robust security measures. Intrusion Detection Systems (IDS) are fundamental for safeguarding VANETs, but their effectiveness in identifying and mitigating intrusions, particularly with evolving attack strategies, remains a challenge. This research focuses on enhancing IDS for VANETs by deploying Convolutional Neural Networks (CNN), a subset of deep learning techniques known for their proficiency in image and signal processing, in intrusion detection. The background underscores the significance of VANETs in modern transportation, emphasizing the growing need for more robust security mechanisms due to increasing connectivity and the advent of Intelligent Transportation Systems (ITS). The primary objective of this research is to assess the performance of CNN-based IDS in the VANET context, specifically in their ability to detect and respond to intrusions effectively and efficiently. The contribution of this research lies in its empirical evaluation of CNN-based IDS for VANETs, offering practical insights into the implementation of advanced security measures in vehicular communication networks. By bridging the gap between theory and application, this research serves as a valuable reference for researchers, practitioners, and policymakers, aiding in the establishment of resilient and secure vehicular communication networks in the era of growing connectivity and sophisticated security challenges.

I. Introduction

Vehicular Ad-hoc Network (VANET) has emerged as a promising communication technology in the field of intelligent transportation systems [1], [2]. The system aims to facilitate safety-critical traffic applications by using Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. These applications include the prevention of accidents resulting from traffic bottlenecks, ice, or other concealed obstacles. Furthermore, VANET provides drivers with the capability to connect to the Internet while in motion, delivers real-time weather information, and offers recommendations for alternative routes. This portable wireless networking, in addition, depends on Vehicle-to-Vehicle and Vehicle-to-Infrastructure (V2V/V2I) communications for information dissemination, but it also utilizes V2X (Vehicle-to-Everything) [3] to enhance the efficiency of message propagation in VANET. This enables vehicles to engage in conversations with pedestrians and other individuals through alternative communication channels. The development of VANET in the transportation sector has garnered attention from both academic and industrial entities. Vehicular Ad hoc Network (VANET) technology is considered a prominent smart technology that has a crucial position within critical infrastructure systems. These systems are of great importance to the nation’s health, security, and economy [4], [5], mostly owing to the extensive data dissemination facilitated by Vehicle-to-Everything (V2X) connections. To ensure the optimal operation of the system, it is important for the smart grid infrastructure to effectively transmit substantial volumes transmission of live information to centers for data within digital channels. This facilitates the monitoring of anomalies and malfunctions, the prediction of energy consumption, and the evaluation of power quality. The utilization of VANET as a temporal computing core in the aforementioned situation might potentially facilitate the expeditious acquisition, administration, and analysis of the escalating volume of data. Therefore, the use of VANET has the potential to enhance the stability of smart grid design by effectively addressing the real-time processing requirements of the grid. Network security issues continue to have a major impact on VANET integration with mission-critical systems that rely on effective storage spaces. delivery, archiving, and recovery of data across networks [6]. However, putting in place systems for intrusion detection that are data-driven is necessary for the development and security of VANET, to mitigate potential attacks and their detrimental penalty. An integrated driven-by-data detection technique is proposed in this research approach that incorporates machine learning techniques. The methodology consists of two main components: The first detection component employs a classification algorithm to identify instances of previously recognized attacks. The second phase of the process is anomaly detection, which utilizes a clustering technique that is grounded in the core sets methodology. This method is used to effectively detect and eliminate nodes that exhibit dishonest behavior. The model’s heightened real-time detection accuracy effectively enhances the security of VANET networks against a diverse array of attacks.