Balaji Prasanalakshmi, Cengiz Korhan, Babu Sangita, Alqahtani Omar, Akleylek Sedat
Department of Computer Science, King Khalid University, Alqaraa, Saudi Arabia.
College of Information Technology, University of Fujairah, Fujairah, United Arab Emirates.
PeerJ Comput Sci. 2024 Oct 31;10:e2366. doi: 10.7717/peerj-cs.2366. eCollection 2024.
The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience. Through the IoV, vehicles can share information about traffic conditions, weather forecasts, road hazards, and other relevant data, fostering smarter, safer, and more efficient transportation networks. Developing, implementing and maintaining sophisticated techniques for detecting attacks present significant challenges and costs, which might limit their deployment, especially in smaller settings or those with constrained resources. To overcome these drawbacks, this article outlines developing an innovative attack detection model for the IoV using advanced deep learning techniques. The model aims to enhance security in vehicular networks by efficiently identifying attacks. Initially, data is collected from online databases and subjected to an optimal feature extraction process. During this phase, the Enhanced Exploitation in Hybrid Leader-based Optimization (EEHLO) method is employed to select the optimal features. These features are utilized by a Complex-Valued Dilated Recurrent Neural Network (CV-DRNN) to detect attacks within vehicle networks accurately. The performance of this novel attack detection model is rigorously evaluated and compared with that of traditional models using a variety of metrics.
车联网（IoV）是物联网（IoT）的一种专门迭代，旨在促进车辆与其环境之间的通信和连接。它利用云计算、无线通信和数据分析等先进技术的力量，在车辆、路边基础设施、交通管理系统和其他实体之间无缝交换实时数据。这种实时数据交换的主要目标包括提高道路安全性、减少交通拥堵、提高交通流效率以及丰富驾驶体验。通过车联网，车辆可以共享交通状况、天气预报、道路危险和其他相关数据，促进形成更智能、更安全、更高效的交通网络。开发、实施和维护用于检测攻击的复杂技术面临重大挑战和成本，这可能会限制它们的部署，尤其是在较小的环境或资源受限的环境中。为了克服这些缺点，本文概述了使用先进的深度学习技术为车联网开发一种创新的攻击检测模型。该模型旨在通过有效识别攻击来增强车辆网络的安全性。最初，从在线数据库收集数据并进行最优特征提取过程。在此阶段，采用基于混合领导者的优化中的增强剥削（EEHLO）方法来选择最优特征。这些特征被复值扩张递归神经网络（CV-DRNN）用于准确检测车辆网络内的攻击。使用各种指标对这种新型攻击检测模型的性能进行了严格评估，并与传统模型进行了比较。
