Ali Muhammad Nadeem, Imran Muhammad, Ullah Ihsan, Raza Ghulam Musa, Kim Hye-Young, Kim Byung-Seo
Department of Software & Communications Engineering, Hongik University, Sejong-si 30016, Republic of Korea.
School of Games/Game Software, Hongik University, Building B, Room # 211, 2639 Sejong-ro, Sejong-si 30016, Republic of Korea.
Sensors (Basel). 2024 Oct 10;24(20):6528. doi: 10.3390/s24206528.
Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.
自动驾驶车辆正在通过集成智能车载单元（OBU）来彻底改变智能交通系统的未来，这些单元可将人为干预降至最低。这些车辆能够与周围环境以及彼此进行通信，共享诸如紧急警报或媒体内容等关键信息。然而，这种通信基础设施容易受到网络攻击，因此需要强大的检测和防御机制。其中，最关键的威胁是拒绝服务（DoS）攻击，它可以针对系统内与自动驾驶车辆通信的任何实体，包括路边单元（RSU）或其他自动驾驶车辆。此类攻击可能会导致毁灭性后果，包括基础设施或自动驾驶车辆本身的服务中断或完全停止。在本文中，我们提出了一种能够在两种场景下检测自动驾驶车辆中DoS攻击的系统：基于基础设施的场景和无基础设施的场景，分别对应车联网通信（V2X）模式3和模式4。对于模式3，我们提出了一种集成学习（EL）方法，而对于模式4环境，我们引入了一种基于八卦学习（GL）的方法。八卦学习和集成学习方法在检测UNSW-NB15数据集上的DoS攻击方面取得了显著成果，效率分别为98.82%和99.16%。此外，与现有方案相比，这些方法表现出了卓越的性能。
