车载网中恶意AP判别方法的研究

发布时间：2018-07-16 16:36

【摘要】：随着车载网的快速发展,WIFI技术的广泛应用,车辆在行驶过程中与路边接入点(Access Point,AP)进行数据通信变得越来越容易。同时,如果敌人把恶意AP部署在移动的车辆上,由于恶意AP车辆的移动性,它可以长时间的保持在用户周围,并且这种恶意AP在客户端看来会拥有更强大的信号强度,更容易使用户受骗上当,因此敌人会有更多时间利用各种攻击手段去窃取用户的隐私信息。针对这种车载恶意AP的特点和危害性,本文提出了一种基于信号强度(RSSI)的判别恶意AP的方法。主要研究成果及贡献如下:(1)RSSI值非常容易受到周围环境的干扰,为了优化RSSI的采样值,本文利用高斯模型去消除RSSI误差较大的部分,并且使用优化加权的方法取得最终值,与均值模型相比,优化加权高斯模型具有更小的误差。(2)针对车载网中不断变化的周围环境,提出了基于RSSI与距离映射的测距算法,该算法把传统单一RSSI与距离的一对一的关系转变为RSSI与距离的一对多的关系,利用一对多的映射关系,实现更适合车载网环境的测距算法,该算法在50米以内,90%以上可以达到误差在2米内,测距误差达到了3.98%。(3)利用提出的基于RSSI值的测距算法,本文提出一种实用性的判别恶意AP的算法,该算法基于RSSI值与GPS位置信息,用户可以验证RSSI值是否匹配AP广播的位置,从而实现对恶意AP的判别。实验结果表明,该算法能够有效地识别道路上的车载恶意AP。(4)基于本文的测距算法,提出一种基于相对定位的判别恶意AP的算法,用户只需利用自身接收的一系列RSSI值就能够对AP的位置进行相对定位,如果恶意AP被定位在用户行驶的车道上,就判定其为恶意AP。实验表明,基于RSSI的相对定位算法误差在5米以内,能够满足对车载恶意AP进行判别的需求。
[Abstract]:With the rapid development of vehicle-borne network and the wide application of WiFi technology, the data communication between vehicle and Access Point AP becomes more and more easy. At the same time, if the enemy deploys a malicious AP on a mobile vehicle, it can stay around the user for a long time because of the mobility of the malicious AP vehicle, and the malicious AP will have a more powerful signal strength in the client's view. It is easier to deceive the user, so the enemy will have more time to steal the user's privacy information by all kinds of attacks. Aiming at the characteristics and harmfulness of this vehicle malicious AP, this paper presents a method based on signal strength (RSSI) to identify malicious AP. The main research results and contributions are as follows: (1) the RSSI value is easily disturbed by the surrounding environment. In order to optimize the sampling value of the RSSI, the Gao Si model is used to eliminate the large error part of the RSSI, and the final value is obtained by the optimization weighting method. Compared with the mean value model, the optimized weighted Gao Si model has less error. (2) aiming at the changing surrounding environment in the vehicle network, a distance measurement algorithm based on RSSI and distance mapping is proposed. In this algorithm, the one-to-one relationship between single RSSI and distance is transformed into the one-to-many relationship between RSSI and distance, and the ranging algorithm is more suitable for vehicle network environment by using one-to-many mapping relationship. The error of the algorithm can reach more than 90% within 50 meters, and the ranging error can reach 3.98. (3) by using the RSSI based ranging algorithm, a practical algorithm for identifying malicious AP is proposed in this paper. The algorithm is based on the RSSI and GPS location information. The user can verify whether the RSSI value matches the broadcast position of AP so as to realize the discrimination of malicious AP. The experimental results show that the algorithm can effectively identify the vehicle malicious APs on the road. (4) based on the ranging algorithm in this paper, an algorithm for identifying malicious AP based on relative location is proposed. The user only needs to use a series of RSSI values to locate the AP. If the malicious AP is located in the driveway of the user, it is determined as malicious AP. The experimental results show that the relative location error based on RSSI is less than 5 meters, which can meet the requirement of identifying malicious AP in vehicle.
【学位授予单位】：黑龙江大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U495;TP309

【相似文献】