Vehicular network anomaly detection based on 2-step deep learning framework

IF 5.8 2区 计算机科学 Q1 TELECOMMUNICATIONS
Nur Cahyono Kushardianto , Soheyb Ribouh , Yassin El Hillali , Charles Tatkeu
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引用次数: 0

Abstract

Intelligent Transportation System (ITS) is one of the newest technologies in the transportation sector that will give hope for better driving safety. Not only in terms of driving safety, but ITS will give also hope for driving comfort. Smart vehicles perchance better versatile to the street circumstances through trade data among vehicles. In case, they can maintain a strategic distance from activity blockage, perilous deterrents, or see activity mishaps prior. The innovation which is meticulously associated with the security of the driver must get extraordinary consideration. V2V-Vehicle-to-Vehicle connection can undermine impedance and indeed attack or anomaly. Many studies have been carried out to address this problem. The primary step is to reinforce the system's capacity to identify anomalies on Vehicular Network. Further, the growing development of machine learning seems to bring hope to support these steps. Within the proposed method, the original of our approach consists in utilizing 2-Step of anomaly detection. This framework is utilizing two classifiers machine learning from two altered preparing data-sets. We appear that the proposed method can make strides essentially attack detection achievement, compared to arrangements depending on a single detection step.

基于两步深度学习框架的车载网络异常检测
智能交通系统(ITS)是交通领域的最新技术之一,它将为提高驾驶安全性带来希望。不仅在驾驶安全方面,智能交通系统还将为驾驶舒适性带来希望。通过车辆之间的数据交换,智能车辆可以更好地适应街道环境。在这种情况下,它们可以与活动阻塞、危险威慑保持战略距离,或提前看到活动事故。与驾驶员安全密切相关的创新必须得到特别的考虑。V2V-车与车之间的连接可能会破坏阻抗,甚至造成攻击或异常。为了解决这个问题,已经开展了许多研究。首要步骤是加强系统识别车辆网络异常的能力。此外,机器学习的不断发展似乎为支持这些步骤带来了希望。在所提出的方法中,我们的原创方法包括利用异常检测的 2 个步骤。该框架利用两个分类器从两个不同的准备数据集进行机器学习。我们发现,与依赖单一检测步骤的安排相比,建议的方法可以在攻击检测成就方面取得长足进步。
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来源期刊
Vehicular Communications
Vehicular Communications Engineering-Electrical and Electronic Engineering
CiteScore
12.70
自引率
10.40%
发文量
88
审稿时长
62 days
期刊介绍: Vehicular communications is a growing area of communications between vehicles and including roadside communication infrastructure. Advances in wireless communications are making possible sharing of information through real time communications between vehicles and infrastructure. This has led to applications to increase safety of vehicles and communication between passengers and the Internet. Standardization efforts on vehicular communication are also underway to make vehicular transportation safer, greener and easier. The aim of the journal is to publish high quality peer–reviewed papers in the area of vehicular communications. The scope encompasses all types of communications involving vehicles, including vehicle–to–vehicle and vehicle–to–infrastructure. The scope includes (but not limited to) the following topics related to vehicular communications: Vehicle to vehicle and vehicle to infrastructure communications Channel modelling, modulating and coding Congestion Control and scalability issues Protocol design, testing and verification Routing in vehicular networks Security issues and countermeasures Deployment and field testing Reducing energy consumption and enhancing safety of vehicles Wireless in–car networks Data collection and dissemination methods Mobility and handover issues Safety and driver assistance applications UAV Underwater communications Autonomous cooperative driving Social networks Internet of vehicles Standardization of protocols.
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