Machine Learning Approach for Multiple Misbehavior Detection in VANET

Grover, Jyoti; Prajapati, Nitesh Kumar; Laxmi, Vijay; Gaur, Manoj Singh

doi:10.1007/978-3-642-22720-2_68

Cited by 114 publications

(62 citation statements)

References 9 publications

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“…It computes the trust of the messages received and checks whether the message is from an honest vehicle or not. Grover et al 12 have presented a security framework in order to categorize numerous misbehaviors in VANET using machine learning technique. It differentiates a malicious node and an honest node based on the features computed by the observer nodes.…”

Section: Data Centric Misbehavior Detection Schemesmentioning

confidence: 99%

Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks

Khan

Agrawal

Silakari

2015

Procedia Computer Science

111

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VANETs enable wireless communication among vehicles and vehicle to infrastructure. Its main objective is to render safety, comfort and convenience on the road. VANET is different from ad-hoc networks due to its unique characteristics. However, because of lack of infrastructure and centralized administration, it becomes vulnerable to misbehaviors. This greatly threatens ing such a useful network must provide adequate security measures for secure communication. The proposed algorithm DMN-Detection of Malicious Nodes in VANETs improves DMV Algorithm in terms of effective selection of verifiers for detection of malicious nodes and hence improves the network performance.

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Section: Data Centric Misbehavior Detection Schemesmentioning

confidence: 99%

Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks

Khan

Agrawal

Silakari

2015

Procedia Computer Science

111

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“…In addition, they are protected from trust attacks by using “hard to success unfortunately to fail their” principle. So, vehicle behavior is very inconspicuous to keep their received credit for all the time . However, researchers in MobiMix have provided user level protection with various attacks, but they did not consider continuous query attack.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the plausibility of receiving hazard messages, in Asuquo et al, security mechanism voting scheme has been proposed. They assumed that most vehicles are trusted which will not certify any false message information . Another study proposed schemes which are based on event reputation system to protect the spread of dishonest traffic threatening messages in road network environments.…”

Section: Related Workmentioning

confidence: 99%

“…So, vehicle behavior is very inconspicuous to keep their received credit for all the time. 25 However, researchers in MobiMix 26 have provided user level protection with various attacks, but they did not consider continuous query attack. This is the limitation of their work in the real world; vehicles in the same group do not all act like malicious ones.…”

Section: Zonesmentioning

confidence: 99%

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MADPTM: Mix zones and dynamic pseudonym trust management system for location privacy

Memon

Mirza

2018

Int J Communication

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Summary In intelligent transportation systems, the messages transmitted and received from one vehicle to another vehicle may enhance location privacy scheme; falsification message forwarding collusion attack, timing, and transition attacks all are serious issues and must be addressed in event trust management. Without which, decision making on event message may endanger the whole transportation system and may even threat the human life. In addition, pseudonym expiry is another critical issue of location privacy. The time interval of decision waiting even has been the critical accident response time. In this paper, we proposed a new novel mix zones and dynamic pseudonym trust management system, called MADPTM, which aims to prevent against attackers under privacy‐enhanced road networks. On shortening the pseudonym expiry, we propose dynamic pseudonym trust management system, with the assistance of reported server. We performed a set of simulations under many trust attack models to evaluate the performance and efficiency of the proposed system. Our proposed MADPTM method proved to be highly resilient to various attacks, and simulation results verified that our method outperforms other existing methods.

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“…They evaluated some decision logic, such as voting, Bayesian inference (BI) and the Dempster-Shafer Theory (DST). Grover et al [14] proposed a framework for misbehavior classification using WEKA, which is efficient in classifying multiple misbehaviors. Besides, Grover et al [15] also proposed an ensemble approach for misbehavior detection.…”

Section: Related Workmentioning

confidence: 99%

Adaptive decision making for improving trust establishment in VANET

2014

The 16th Asia-Pacific Network Operations and Management Symposium

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In existing trust establishment researches, the decision making accuracy and decision delay of received event message are both the outstanding problems in VANETs. Especially on the decision delay, the delay of alert event might lead to traffic accident and injury. In this paper, we propose an adaptive decision making model which can make decision quickly by the effective and quick assistance of the RSUs. To evaluate the detection accuracy and efficiency of our proposed system, we conducted several simulations under different trust attacks. In the experiment results, the purpose model can shorten decision delay while raising the detection accuracy.

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Machine Learning Approach for Multiple Misbehavior Detection in VANET

Cited by 114 publications

References 9 publications

Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks

Detection of Malicious Nodes (DMN) in Vehicular Ad-Hoc Networks

MADPTM: Mix zones and dynamic pseudonym trust management system for location privacy

Adaptive decision making for improving trust establishment in VANET

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