CEAP: SVM-based intelligent detection model for clustered vehicular ad hoc networks

Wahab, Omar Abdel; Mourad, Azzam; Otrok, Hadi; Bentahar, Jamal

doi:10.1016/j.eswa.2015.12.006

Cited by 108 publications

(50 citation statements)

References 34 publications

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“…In particular, many activities have been done, such as dataset preparation, environmental noise injection, communication simulation, and misbehavior simulation. Similar to related studies [2,20,29,35,76,77], the Matlab tool [78] has been used for simulating the environmental noises, vehicle communication, and misbehaving vehicles.…”

Section: Methodsmentioning

confidence: 99%

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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 The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles.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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Section: Methodsmentioning

confidence: 99%

“…Road 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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Moreover, a Nash equilibrium was examined. In Reference , the cooperative monitoring of communication between the vehicles was extended to build a training data set, which was analyzed by support vector machine (SVM) learning techniques in an incremental fashions to classify cooperative and malicious vehicular nodes. The model was based on top of QoS‐OSLR protocol and used for the maintenance of clusters and increasing the lifetime of the network by using mobility metric protocols during cluster formations.…”

Section: Countermeasures To Handle Attacks In Vanetmentioning

confidence: 99%

A systematic review on security issues in vehicular ad hoc network

Tanwar

Vora

Tyagi

et al. 2018

Security and Privacy

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Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation system to provide safety and security alongwith reduction in traffic jam using vehicle‐to‐vehicle (V2V) and vehicle‐to‐roadside (V2R) unit communications. However, one of the major concerns in VANETs is to maintain the privacy and security of the sensitive information shared between vehicles and end‐users, as information leakage can influence any implemented solution in this environment. Keeping view on the above‐mentioned points, this paper provides a detailed survey on various types of attacks and possible solutions to handle these attacks. Using the proposed survey, a detailed taxonomy based upon the types of attacks and possible solutions to handle these attacks is proposed. Finally, various emerging open issues and challenges along with the security threats are identified and discussed in the paper. These open research issues motivate the communities to design new solutions to mitigate the existing new security threats in this area.

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“…They overcome the mobility and network vulnerability of cluster member nodes during the initialization phase of distributed ad hoc networks, and select the appropriate cluster head node according to the mobility state and trust level of nodes, which effectively reduce the communication overhead. In addition, based on the existing dynamic link routing protocol, Wahab et al [10] reduced the size of training dataset by restricting data collection and storage, and adopted the classification technology of support vector machine (SVM) to implement collaborative monitoring between vehicles in online and incremental manner, and reduced the cost of exchanging results between nodes by transporting the final decision in some data clusters. This scheme improves the accuracy of intrusion detection to a certain extent.…”

mentioning

confidence: 99%

An Intrusion Detection Based on Bayesian Game Theory for UAV Network

Sun¹,

Wang²,

Da³

et al. 2018

Proceedings of the 11th EAI International Conference on Mobile Multimedia Communications

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Unmanned aerial vehicles (UAVs) have enormous potential in public, civil and military fields, so the security of UAV networks is attracting increasing people's attention. The existing schemes to network intrusion detection have two main limitations: a high false alarm rate and computational overhead. In this paper, aiming at the problem of UAV network security, an intrusion detection scheme based on Bayesian game theory is proposed. Bayesian game means that game participants do not have complete information on the profit function of the opponent. In this paper, incomplete information means that the IDS agent is not sure of the type of attacker and the attacker is not sure whether its neighbor node is an IDS agent or not. The simulation and experimental comparison show that the proposed scheme achieves a high detection rate of intrusion. In addition, the communication cost of the network is the lowest.

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CEAP: SVM-based intelligent detection model for clustered vehicular ad hoc networks

Cited by 108 publications

References 34 publications

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

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

A systematic review on security issues in vehicular ad hoc network

An Intrusion Detection Based on Bayesian Game Theory for UAV Network

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