Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page

Sensor Based Framework for Secure Multimedia Communication in VANET

Secure multimedia communication enhances the safety of passengers by providing visual pictures of accidents and danger situations. In this paper we proposed a framework for secure multimedia communication in Vehicular Ad-Hoc Networks (VANETs). Our proposed framework is mainly divided into four components: redundant information, priority assignment, malicious data verification and malicious node verification. The proposed scheme jhas been validated with the help of the NS-2 network simulator and the Evalvid tool

Open issues in differentiating misbehavior and anomalies for VANETs

This position paper proposes new challenges in data-centric misbehavior detection for vehicular ad-hoc networks (VANETs). In VANETs, which aim to improve safety and efficiency of road transportation by enabling communication between vehicles, an important challenge is how vehicles can be certain that messages they receive are correct. Incorrectness of messages may be caused by malicious participants, damaged sensors, delayed messages or they may be triggered by software bugs. An essential point is that due to the wide deployment in these networks, we cannot assume that all vehicles will behave correctly. This effect is stronger due to the privacy requirements, as those requirements include multiple certificates per vehicle to hide its identity. To detect these incorrect messages, the research community has developed misbehavior data-centric detection mechanisms, which attempt to recognize the messages by semantically analyzing the content. The detection of anomalous messages can be used to detect and eventually revoke the certificate of the sender, if the message was malicious. However, this approach is made difficult by rare events –such as accidents–, which are essentially anomalous messages that may trigger the detection mechanisms. The idea we wish to explore in this paper is how attack detection may be improved by also considering the detection of specific types of anomalous events, such as accidents

Host Based Intrusion Detection for VANETs: A statistical approach to Rogue Node Detection

In this paper, an intrusion detection system (IDS) for vehicular ad hoc networks (VANETs) is proposed and evaluated. The IDS is evaluated by simulation in the presence of rogue nodes (RNs) that can launch different attacks. The proposed IDS is capable of detecting a false information attack using statistical techniques effectively and can also detect other types of attacks. First, the theory and implementation of the VANET model that is used to train the IDS is discussed. Then, an extensive simulation and analysis of our model under different traffic conditions is conducted to identify the effects of these parameters in VANETs. In addition, the extensive data gathered in the simulations are presented using graphical and statistical techniques. Moreover, RNs are introduced in the network, and an algorithm is presented to detect these RNs. Finally, we evaluate our system and observe that the proposed application-layer IDS based on a cooperative information exchange mechanism is better for dynamic and fast-moving networks such as VANETs, as compared with other techniques available

TFDD: A trust-based framework for reliable data delivery and DoS defense in VANETs

[EN] A trust establishment scheme for enhancing inter-vehicular communication and preventing DoS attacks `TFDD¿ is proposed in this paper. Based on a developed intrusion detection module (IDM) and data centric verification, our framework allows preventing DDoS attacks and eliminating misbehaving nodes in a distributed, collaborative and instantaneous manner. In addition, a trusted routing protocol is proposed that, using context-based information such as link stability and trust information, delivers data through the most reliable way. In this study, the simulation results obtained demonstrate the effectiveness of our trust framework at detecting dishonest nodes, as well as malicious messages that are sent by honest or dishonest nodes, after a very low number of message exchanges. Furthermore, colluding attacks are detected in a small period of time, which results in network resources being released immediately after an overload period. We also show that, in a worst-case scenario, our trust-based framework is able to sustain performance levels, and outperforming existing solutions such as T-CLAIDS and AECFV.Kerrache, CA.; Lagraa, N.; Tavares De Araujo Cesariny Calafate, CM.; Lakas, A. (2017). TFDD: A trust-based framework for reliable data delivery and DoS defense in VANETs. Vehicular Communications. 9:254-267. doi:10.1016/j.vehcom.2016.11.010S254267

Secure and robust multi-constrained QoS aware routing algorithm for VANETs

Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service

Secured information dissemination and misbehavior detection in VANETs

In a connected vehicle environment, the vehicles in a region can form a distributed network (Vehicular Ad-hoc Network or VANETs) where they can share traffic-related information such as congestion or no-congestion with other vehicles within its proximity, or with a centralized entity via. the roadside units (RSUs). However, false or fabricated information injected by an attacker (or a malicious vehicle) within the network can disrupt the decision-making process of surrounding vehicles or any traffic-monitoring system. Since in VANETs the size of the distributed network constituting the vehicles can be small, it is not difficult for an attacker to propagate an attack across multiple vehicles within the network. Under such circumstances, it is difficult for any traffic monitoring organization to recognize the traffic scenario of the region of interest (ROI). Furthermore, even if we are able to establish a secured connected vehicle environment, an attacker can leverage the connectivity of individual vehicles to the outside world to detect vulnerabilities, and disrupt the normal functioning of the in-vehicle networks of individual vehicles formed by the different sensors and actuators through remote injection attacks (such as Denial of Service (DoS)). Along this direction, the core contribution of our research is directed towards secured data dissemination, detection of malicious vehicles as well as false and fabricated information within the network. as well as securing the in-vehicle networks through improvisation of the existing arbitration mechanism which otherwise leads to Denial of Service (DoS) attacks (preventing legitimate components from exchanging messages in a timely manner). --Abstract, page iv