333 research outputs found

    Data-centric Misbehavior Detection in VANETs

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    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

    Misbehavior Detection in Vehicular Ad-hoc Networks

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    In this paper we discuss misbehavior detection for vehicular ad-hoc networks (VANETs), a special case of cyber-physical systems (CPSs). We evaluate the suitability of existing PKI approaches for insider misbehavior detection and propose a classification for novel detection schemes. Cyber-physical systems (CPSs) are digital systems that are closely embedded into the physical world with which they interact through sensors and actuators. In contrast to classical embedded systems, they often form networks with a large number of sensor or actuator devices. These devices sense information, process it in a distributed system, and then influence the physical world using actuators. Notable examples of CPS are wireless sensor networks (WSNs), smart factories, distributed eHealth systems, and VANETs. In this paper, we focus on VANETs, which are a prime example for CPS and will soon be deployed on a large scale. Vehicular ad-hoc networks (VANETs) are networks that are created by equipping vehicles with wireless transmission equipment. VANETs offer great potential to improve road safety and to provide information and entertainment applications for drivers and passengers

    Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)

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    Cooperative Adaptive Cruise Control (CACC) is one of the driving applications of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and faster transportation through cooperative behavior between vehicles. In CACC, vehicles exchange information, which is relied on to partially automate driving; however, this reliance on cooperation requires resilience against attacks and other forms of misbehavior. In this paper, we propose a rigorous attacker model and an evaluation framework for this resilience by quantifying the attack impact, providing the necessary tools to compare controller resilience and attack effectiveness simultaneously. Although there are significant differences between the resilience of the three analyzed controllers, we show that each can be attacked effectively and easily through either jamming or data injection. Our results suggest a combination of misbehavior detection and resilient control algorithms with graceful degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular Networking Conference (VNC

    Enhanced Position Verification for VANETs using Subjective Logic

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    The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE~1609.2 standard, which provides typical integrity guarantees. However, the correctness of message contents is still one of the main challenges of applying dependable and secure vehicular ad-hoc networks. One important use case is the validity of position information contained in messages: position verification mechanisms have been proposed in the literature to provide this functionality. A more general approach to validate such information is by applying misbehavior detection mechanisms. In this paper, we consider misbehavior detection by enhancing two position verification mechanisms and fusing their results in a generalized framework using subjective logic. We conduct extensive simulations using VEINS to study the impact of traffic density, as well as several types of attackers and fractions of attackers on our mechanisms. The obtained results show the proposed framework can validate position information as effectively as existing approaches in the literature, without tailoring the framework specifically for this use case.Comment: 7 pages, 18 figures, corrected version of a paper submitted to 2016 IEEE 84th Vehicular Technology Conference (VTC2016-Fall): revised the way an opinion is created with eART, and re-did the experiments (uploaded here as correction in agreement with TPC Chairs

    Misbehavior detection in vehicular ad-hoc networks

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    In this paper we discuss misbehavior detection for vehicular ad-hoc networks (VANETs), a special case of cyber-physical systems (CPSs). We evaluate the suitability of existing PKI approaches for insider misbehavior detection and propose a classification for novel detection schemes

    Open issues in differentiating misbehavior and anomalies for VANETs

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    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
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