Performance Investigation of DMV (Detecting Malicious Vehicle) and D&amp;PMV (Detection and Prevention of Misbehave/Malicious Vehicles): Future Road Map

Kadam, Megha; Limkar, Suresh

doi:10.1007/978-3-319-02931-3_43

Cited by 12 publications

(13 citation statements)

References 7 publications

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“…This method maintains stability and quality of service with increase in detection probability and decreases the number of selfish nodes and false negatives. Kadam et al have presented a new approach 14 for not solely the detection of malicious vehicles attack, additionally their prevention from the VANET. It is an improvement of the Detection of Malicious Vehicles (DMV) algorithm 3 .…”

Section: Node Centric Misbehavior Detection Schemesmentioning

confidence: 99%

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

Khan

Agrawal

Silakari

2015

Procedia Computer Science

108

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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: Node Centric Misbehavior Detection Schemesmentioning

confidence: 99%

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

Khan

Agrawal

Silakari

2015

Procedia Computer Science

108

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“…The experimental result shows that the proposed solution can detect malicious attackers at very high movements. In [35] the prevention mechanism is added to [34], and the selection of the verifier is improved in [36].…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

“…In [35], Kadam et al made improvements to the algorithm proposed in [34] by adding the prevention and isolation mechanism of a Black Hole from the network. Almost the entire algorithm proposed in [35] is the same as in [34]. The difference lies in the additional parameter used for the isolation of an attacker and the alarm used, which is contained in the identity of the malicious node broadcasted across the network.…”

Section: Background and Related Workmentioning

confidence: 99%

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Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles

et al. 2020

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Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput.

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“…Many more advanced routing techniques have been developed for vehicular networks in recent years. 4 To aid the process of establishing and maintaining communication connections, location information (i.e. via Global Positioning System (GPS)) is used in some of the routing techniques.…”

Section: Introductionmentioning

confidence: 99%

On-demand Misbehavior Detection for Vehicular Ad Hoc Network

Lin

Chen

2016

International Journal of Distributed Sensor Networks

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Maintaining communication links of an established communication path that extends between source and destination nodes is a significant challenge in Vehicular Ad Hoc Networks due to movement of the vehicles. Operations of Vehicular Ad Hoc Network rely on the collaboration of participating nodes to route data for each other. A critical requirement on the nodes is to cooperate with each other for successful data transmission. Thus, the impact of malicious and selfish users must be detected to ensure the operations of Vehicular Ad Hoc Network. In this article, we proposed Ondemand Misbehavior Detection technique for vehicle-to-vehicle communication. We adapt two location-based routing protocols, Contention-Based Forwarding and Connectionless Approach for Streets, to our On-demand Misbehavior Detection. Various experiments are conducted to show the effectiveness and efficiency of the proposed On-demand Misbehavior Detection technique. The simulation and analytical results showed that the proposed technique is very effective in detecting misbehaving nodes.

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Performance Investigation of DMV (Detecting Malicious Vehicle) and D&PMV (Detection and Prevention of Misbehave/Malicious Vehicles): Future Road Map

Cited by 12 publications

References 7 publications

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

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

Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles

On-demand Misbehavior Detection for Vehicular Ad Hoc Network

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