Localization of multiple jamming attackers in vehicular ad hoc network

Abstract

In vehicular ad hoc network, wireless jamming attacks are easy to be launched in the control channel and can cause serious influence on the network performance which may cause further safety accidents. In order to address the issue of wireless jamming attacks, a new technique which localizes the jamming attackers and prevents vehicles from jamming through human intervention is proposed. In this article, we propose a range-free approach to localize the source of the attacker and determine the number of jamming attackers. The data set is the locating information and the jamming detection information associated with each vehicle. Then, we formulate the problem of determining the number of attackers as a multiclass detection problem. We define the incorrectly classified area and use it to measure the distance between samples and centroids in fuzzy c-means algorithm. We further determine the number of jamming attackers through the coverage rate of beaconing circles and utilize weight-based fuzzy c-means to classify the data set. When the data set is classified as acceptable, we further explore the means of using particle swarm optimization algorithm to calculate the positional coordinates of each attacker. We simulate our techniques in MATLAB, and both urban traffic area and open area are considered in our simulation. The experimental results suggest that the proposed algorithm can achieve high precision when determining the number of attackers while the result of the classified performance is always satisfying. Our localization results lead to higher accuracy than other existing solutions. Also, when the data set is limited, the chances of taking accurate localization are higher than other measures.

Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network

“…Many VANET features are used in the literature to recognize cases of intentional jamming, such as packet delivery rate 33 and GPS information. 34 The selection of features is made according to the context of detection solutions that could be deployed in different ways. Afterwards, certain machine learning method is selected to analyze the VANET data and classify the malicious nodes from benign nodes, as shown in Figure 1.…”

Improved Security Particle Swarm Optimization (PSO) Algorithm to Detect Radio Jamming Attacks in Mobile Networks

“…Jamming devices are better designed to provide the best possible network coverage to cause harm [16]. The jamming source will be easier to find if each node's coverage area is minimized.…”

Improving Big Data Clustering for Jamming Detection in Smart Mobility

“…In [34], the advantages of K-means were used to predict the number of multiple jamming attackers and ensure the preset functions of VANET. In [33], an anti-jamming method based on fuzzy c-means was proposed to determine the localization and number of jamming attackers. Accordingly, the cluster analysis process simplifies data manipulation by finding similar structures in the data and classifying each object according to its nature.…”

“…Figure 1 presents a general process of attack detection based on the application of data clustering, where a predefined list of features is extracted from vehicular data to detect the characteristics of jamming attacks. The selection of features is according to the context of the proposed anti-jamming solutions, for example, GPS information is used to recognize cases of intentional jamming [33]. After that, the clustering method can be used to analyze vehicular data and classify timely the malicious nodes from benign ones.…”