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首页> 外文期刊>Computers >Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
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Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

机译：自驾车网络中灰洞和冲刺攻击的智能入侵检测

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Vehicular ad hoc networks (VANETs) play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs) exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS) that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.
机译：车载自组织网络（VANET）在自动驾驶和半自动驾驶汽车的成功发展中发挥着至关重要的作用，它们提高了安全性和舒适性。这样的车辆在很大程度上依赖于通过数据控制和协作意识消息（CAM）交换与周围环境的外部通信。 VANET可能会遭受许多攻击，例如灰洞，黑洞，虫洞和紧急攻击。这项工作提出了一种智能入侵检测系统（IDS），该系统依靠异常检测来保护外部通信系统免受灰洞和紧急攻击。这些攻击旨在破坏车辆与路边单位之间的传输。 IDS使用从网络模拟器中生成的跟踪文件中获得的功能，并由前馈神经网络和支持向量机组成。此外，本文研究了一种新颖的系统响应的使用，该响应用于保护车辆遇到恶意行为时的行为。我们对所提出的检测系统的仿真表明，所提出的方案具有出色的检测率，并且减少了误报。在正常和异常情况下，已经使用四个性能指标对这个安全模式响应系统进行了评估，即接收的数据包，数据包的传输比率，丢失的数据包和平均端到端延迟。

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