Detection of Sybil attack in vehicular ad hoc networks by analyzing network performance

<span>Vehicular ad hoc network (VANET) is an emerging technology which can be very helpful for providing safety and security as well as for intelligent transportation services. But due to wireless communication of vehicles and high mobility it has certain security issues which cost the safety and security of people on the road. One of the major security concerns is the Sybil attack in which the attacker creates dummy identities to gain high influence in the network that causes delay in some services and fake voting in the network to misguide others. The early detection of this attack can prevent people from being misguided by the attacker and save them from getting into any kind of trap. In this research paper, Sybil attack is detected by first applying the Poisson distribution algorithm to predict the traffic on the road and in the second approach, analysis of the network performance for packet delivery ratio (PDR) is performed in malign and benign environment. The simulation result shows that PDR decreases in presence of fake vehicles in the network. Our approach is simple and effective as it does not require high computational overhead and also does not violate the privacy issues of people in the network.</span>

Trajectory as an Identity: Privacy-Preserving and Sybil-Resistant Authentication for Internet of Vehicles

With the advancement of the 5G network, the Internet of Vehicles (IoV) is becoming more and more attractive for academic researchers and industrial. A main challenge of IoV is to guarantee the authenticity of messages and protect drivers’ privacy simultaneously. The majority of privacy-preserving authentication schemes for IoV adopt pseudonyms or group signatures to achieve a balance between security and privacy. However, defending the Sybil attacks in these schemes is challenging. In this work, we propose a novel privacy-preserving authentication scheme for announcement messages, which utilizes the trajectories of vehicles as their identities. When an authenticated message is verified, the verifier is convinced that the message is generated by a vehicle that has a unique masked trajectory. Meanwhile, the real trajectories of vehicles are kept private. In particular, our scheme achieves Sybil attack resistance without the limitation of trajectory length even when the attacker is allowed to use cloud services.

Identifying Nodes with Maladaptive Behavior in WSN’s by Using Hybrid Incursion Identification Approach for Secure Wireless

In this study, to detect attacks of WSNs, a Hybrid Incursion Identification Approach (HIIA) is proposed. To reduce the amount of Energy Consumption (EC) of the sensor nodes, the HIIA mechanism utilizes a cluster-oriented approach with the LEACH protocol. For misuse observation and anomaly recognition, with MPNN (Multilayer Perceptron Neural Network) depended on fuzzy rule sets, HIIA structure is utilized. To refer to various varieties of attackers and to harmonize the identification results, with appendicle NN, FFNN (Feed Forward Neural Network) is utilized, that means Sybil Attack (SA), Hello Flood Attack (HFA) and Wormhole Attack (WA). To detect a SA, Improved SA Algorithm developed. Similarly, to detect a WA, that particular method is developed by Wormhole Anti-Hybrid Technique. Using the distance and power of the signal, HFA is detected. An exploratory research is conveyed out in a group of nodes. The nodes that misbehave in them are all determined. This proposed method, detects the performance of the accuracy, precision-recall and EC. This proposed method also finds the WA Detection Rate, HFA detection rate and the SA Detection Rate, respectively.