An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications

Ghaleb, Fuad A.; Zainal, Anazida; Rassam, Murad A.; Mohammed, Fathey

doi:10.1109/ains.2017.8270417

Cited by 66 publications

(51 citation statements)

References 27 publications

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“…Ghaleb et al [107] propose a similar approach to Grover et al, using neural networks. The authors describe how vehicles collect information from their environment using sensors, which is then shared through communication (i.e., through V2V communication).…”

Section: B Data-centric Mechanismsmentioning

confidence: 99%

Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

Heijden

Dietzel

Leinmüller

et al. 2019

IEEE Commun. Surv. Tutorials

193

148

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Cooperative Intelligent Transportation Systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and infrastructure, thereby creating a highly dynamic and heterogeneously managed ad-hoc network. It is these network properties that make it a challenging task to protect integrity of the data and guarantee its correctness. A major component is the problem that traditional security mechanisms like PKIbased asymmetric cryptography only exclude outsider attackers that do not possess key material. However, because attackers can be insiders within the network (i.e., possess valid key material), this approach cannot detect all possible attacks. In this survey, we present misbehavior detection mechanisms that can detect such insider attacks based on attacker behavior and information analysis. In contrast to well-known intrusion detection for classical IT systems, these misbehavior detection mechanisms analyze information semantics to detect attacks, which aligns better with highly application-tailored communication protocols foreseen for cITS. In our survey, we provide an extensive introduction to the cITS ecosystem and discuss shortcomings of PKI-based security. We derive and discuss a classification for misbehavior detection mechanisms, provide an in-depth overview of seminal papers on the topic, and highlight open issues and possible future research trends.

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Section: B Data-centric Mechanismsmentioning

confidence: 99%

Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

Heijden

Dietzel

Leinmüller

et al. 2019

IEEE Commun. Surv. Tutorials

193

148

View full text Add to dashboard Cite

show abstract

“…Moreover, the attackers can leverage the model to search for a successful attack. A more general model was proposed in Reference [68], where the datasets were generated under different communication status and environmental noises scenarios. The context was represented by behavioral and data-centric features that were extracted offline, assuming the availability of sufficient information.…”

Section: Related Workmentioning

confidence: 99%

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

et al. 2019

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Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. IntroductionRoad collisions are increasing, and they are being expected to be the fifth leading cause of death by 2030 [1,2]. Annually, millions of people lose their lives on roads worldwide due to traffic accidents [1], with 40 times more suffering from injuries. These accidents are also the main cause of traffic congestion, which in turn has a great impact on the economy [3,4], and billions of dollars are lost due to the treatment of injuries, loss of property, lost working hours, and high fuel consumption [5]. Several studies reveal that more than 95%...

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“…There have been mainly three approaches to solving the problem, i.e., behavior analysis [11,14,15], sensor-fusion [16][17][18] and communication-based constraint check [5,12,19]. These approaches are not mutually exclusive, so they can be used alone, or in combination.…”

Section: Related Workmentioning

confidence: 99%

“…It also used maps to check if a vehicle can navigate through the claimed position. Ghaleb et al [15] used neural networks to find misbehavior in the communicated information. The local dynamic map (LDM) is constructed from the shared information, and each message is determined legitimate or malicious based on the historical behavior of the model.…”

Section: Vehicle Dynamics-based Validationmentioning

confidence: 99%

Vehicle-to-Vehicle (V2V) Message Content Plausibility Check for Platoons through Low-Power Beaconing

Kim

2019

Sensors

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Although the IEEE Wireless Access in Vehicular Environment (WAVE) and 3GPP Cellular V2X deployments are imminent, their standards do not yet cover an important security aspect; the message content plausibility check. In safety-critical driving situations, vehicles cannot blindly trust the content of received safety messages, because an attacker may have forged false values in it in order to cause unsafe response from the receiving vehicles. In particular, the attacks mounted from remote, well-hidden positions around roads are considered the most apparent danger. So far, there have been three approaches to validating V2X message content: checking based on sensor fusion, behavior analysis, and communication constraints. This paper discusses the three existing approaches. In addition, it discusses a communication-based checking scheme that supplements the existing approaches. It uses low-power transmission of vehicle identifiers to identify remote attackers. We demonstrate its potential address in the case of an autonomous vehicle platooning application. without knowing whether the contents of the messages sent by the nearby vehicles are genuine or fake. In case an attacker has access to the device with a valid credential, she could forge an erroneous message and transmit it, which will not be filtered by the certificate check. Then, the attacker could cause the receiver vehicles to inappropriately react to the faulty message content and possibly face critical consequences such as collisions. Since tampering with the sensory input data such as GPS position information to be transferred by communication protocols is easier than breaking the protocols or the cryptographic system, this type of attack may be more beneficial for the attackers.Although the content fabrication attacks from a close distance can easily be refuted by the multitude of on-board sensors such as radars, cameras, and lidars on the vehicle, remote attacks out of the line of sight (LoS) of these sensors cannot be adequately filtered. A notable case would be roadside attacks, where the attacker can broadcast false information to the passing vehicles. For example, the attacker can fabricate false positional messages of a "ghost vehicle" to cause a multi-car pile-up in a long platoon [5]. Therefore, to obtain the users' confidence in this technology, the credibility of the V2X messages from remote locations out of the range of the LoS sensors should be checked.Below, we propose a solution where vehicles employ low-power beaconing messages (called "whispers") to judge whether the vehicles can trust the contents of the received vehicle messages or not. This paper particularly focuses on the data fabrication attacks on the position information, a crucial and endangered subpart of the system [6]. We demonstrate the value of the proposed approach by considering a promising use case of V2X, platooning, so that the platoons can maintain traffic efficiency in the situation that the roadside attacker transmits fake messages. Although studies applying vehicular ...

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An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications

Cited by 66 publications

References 27 publications

Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Vehicle-to-Vehicle (V2V) Message Content Plausibility Check for Platoons through Low-Power Beaconing

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