Information Fusion-based Cybersecurity Threat Detection for Intelligent Transportation System

Abdullahi Chowdhury, R. Naha, Shahriar Kaisar, M. Khoshkholghi, Kamran Ali, A. Galletta
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Abstract

Intelligent Transportation Systems (ITS) are sophisticated systems that leverage various technologies to increase the safety, efficiency, and sustainability of transportation. By relying on wireless communication and data collected from diverse sensors, ITS is vulnerable to cybersecurity threats. With the increasing number of attacks on ITS worldwide, detecting and addressing cybersecurity threats has become critically important. This need will only intensify with the impending arrival of autonomous vehicles. One of the primary challenges is identifying critical ITS assets that require protection and understanding the vulnerabilities that cyber attackers can exploit. Additionally, creating a standard profile for ITS is challenging due to the dynamic traffic pattern, which exhibits changes in the movement of vehicles over time. To address these challenges, this paper proposes an information fusion-based cybersecurity threat detection method. Specffically, we employ the Kalman filter for noise reduction, Dempster-Shafer decision theory and Shannon’s entropy for assessing the probabilities of traffic conditions being normal, intruded, and uncertain. We utilised Simulation of Urban Mobility (SUMO) to simulate the Melbourne CBD map and historical traffic data from the Victorian transport authority. Our simulation results reveal that information fusion with three sensor data is more effective in detecting normal traffic conditions. On the other hand, for detecting anomalies, information fusion with two sensor data is more efficient.
基于信息融合的智能交通系统网络安全威胁检测
智能交通系统(ITS)是一种复杂的系统,它利用各种技术来提高交通的安全性、效率和可持续性。由于依赖无线通信和从各种传感器收集的数据,ITS容易受到网络安全威胁。随着全球范围内对智能交通系统的攻击越来越多,检测和应对网络安全威胁变得至关重要。随着自动驾驶汽车的到来,这种需求只会加剧。主要挑战之一是确定需要保护的关键ITS资产,并了解网络攻击者可以利用的漏洞。此外,由于交通模式是动态的,车辆的移动会随着时间的推移而变化,因此为ITS创建标准配置文件是具有挑战性的。针对这些挑战,本文提出了一种基于信息融合的网络安全威胁检测方法。具体来说,我们采用卡尔曼滤波降噪,邓普斯特-谢弗决策理论和香农熵来评估交通状况正常,入侵和不确定的概率。我们利用城市交通模拟(SUMO)来模拟墨尔本CBD地图和维多利亚州交通管理局的历史交通数据。仿真结果表明,三种传感器数据的信息融合能够更有效地检测正常交通状况。另一方面,对于异常检测,两个传感器数据的信息融合更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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