Modeling and Simulation of Blackhole Attack Detection using Multipath Routing in WSN-based IoV

Won-Jin Chung, T. Cho
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Abstract

The Internet of Vehicles (IoV) is a technology that combines the Internet of Things and an intelligent transportation system (ITS), and it is being studied to provide driver convenience and reduce traffic accidents. Autonomous vehicles use advanced driver assistance systems sensors such as cameras, riders, and radar to recognize the road environment. In addition, autonomous vehicles use a high definition map (HDmap) to search a driving route, and use vehicle to everything (V2X) communication technology to acquire external information to drive safely. However, HD-maps and V2X communication have a lot of influence on the external environment. To solve this problem, a scheme for applying a wireless sensor network (WSN) to an ITS has been proposed. WSNs can detect wild animals, so building infrastructure in wild animal haunting areas can prevent road kills caused by autonomous vehicles. However, the sensor node of a WSN is deployed outside and has the disadvantage of being vulnerable to security because it uses wireless communication. If a black hole attack is attempted on the WSN used for the IoV, the message may not be delivered and damage from a car accident may occur. To solve this problem, the IoV must be efficiently authenticated using public keys, and WSN must detect and respond to attacks to deliver accurate information. The proposed scheme prevents accidents by detecting a black hole attack through base station and initializing the damaged node by performing secondary verification through the IoV. The proposed scheme evaluates the performance by simulation using discrete event system specifications. The proposed scheme shows a detection rate of 70% when a black hole attack is attempted with 87.0414% probability through the experimental results. Keywords— Discrete Event System Specification; Internet of Vehicle; Network Security; Wireless Sensor Network
基于wsn的车联网多径路由黑洞攻击检测建模与仿真
车联网(IoV)是将物联网和智能交通系统(ITS)相结合的技术,旨在为驾驶员提供便利和减少交通事故,目前正在研究中。自动驾驶汽车利用摄像头、驾驶员、雷达等先进的驾驶辅助系统传感器来识别道路环境。此外,自动驾驶汽车使用高清地图(HDmap)搜索行驶路线,并使用车辆到一切(V2X)通信技术获取外部信息,以确保安全驾驶。然而,高清地图和V2X通信对外部环境有很大的影响。为了解决这一问题,提出了一种将无线传感器网络应用于智能交通系统的方案。无线传感器网络可以检测野生动物,因此在野生动物出没的地区建设基础设施可以防止自动驾驶汽车造成的道路死亡。然而,WSN的传感器节点部署在外部,由于使用无线通信,存在易受安全威胁的缺点。如果对用于IoV的WSN进行黑洞攻击,可能会导致消息无法传递,并可能造成交通事故的损害。为了解决这一问题,必须使用公钥对车联网进行有效的身份验证,WSN必须检测并响应攻击,以提供准确的信息。该方案通过基站检测黑洞攻击,并通过车联网进行二次验证,初始化受损节点,防止事故发生。该方案利用离散事件系统规范通过仿真来评估性能。实验结果表明,当尝试以87.0414%的概率进行黑洞攻击时,该方案的检测率为70%。关键词:离散事件系统规范;车联网;网络安全;无线传感器网络
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