333 research outputs found
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
Misbehavior Detection in Vehicular Ad-hoc Networks
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)
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
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
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
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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