Modeling Sybil attacker behavior in VANETs

Kafil, Parastoo; Fathy, Mahmoud; Lighvan, ‪Mina Zolfy

doi:10.1109/iscisc.2012.6408215

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Beacons and Context Awareness Messages (CAMs) are the mechanism by which a vehicle node in a VANET routinely communicates with its neighbors in order to provide the latter with information regarding the location of the vehicles that are located in close proximity to it. CAMs are often provided (in broadcast) at a rate of one per hundred millisecond via a single-hop connection that can span a distance of at least one kilometer [19]. The contents of the communications include not only the identities of the nodes, but also their positions, times, speeds, and accelerations, in addition to information regarding mobility and context.…”

Section: A Movement Matrix Definedmentioning

confidence: 99%

Sybil Attack Detection in VANET Using Machine Learning Approach

Kakulla¹,

Malladi²

2022

ISI

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VANET (Vehicular Ad-hoc Network) is a subclass of MANET in which many cars can connect with one another via node to node or equipment erected on the side of the road. However, due to the adaptability of centres and the unexpected trade in geography, there may be opportunities for attacks in VANET. One of the ostensible assaults is the Sybil attack, in which the attacker fabricates unequivocally unique equal personalities to undermine the value of VANET. Sybil creates fictitious identities inside the community as well in order to sabotage attempts to mediate conversations between community nodes. Sybil assaults have an impact on carrier transportation in relation to things like traffic congestion, road safety, and multimedia entertainment. VANETs therefore announce a security mechanism to protect you from Sybil attacks. In this regard, this work puts forth the SDTC method, which completely relies on machine learning techniques to prevent Sybil assaults in VANETs. In order to reduce identification time, increase detection accuracy, and enhance scalability, the SDTC (Sybil node detecting the use of Classification) mechanism uses a few vehicle-specific Extreme Learning Machine (ELM) features. The results suggest that SDTC is a suitable strategy to reduce Sybil assaults and sustain provider service in VANETs.

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Section: A Movement Matrix Definedmentioning

confidence: 99%

Sybil Attack Detection in VANET Using Machine Learning Approach

Kakulla¹,

Malladi²

2022

ISI

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“…Consequently, in our insight, this paper shows the worth examination since it demonstrates the execution time of MVQQ algorithm in different scenarios of VANET. [6] The objective was to accomplish nearby security by utilizing locally available radar to recognize neighbors and to affirm their declared GPS organizes. Every vehicle produces data about the condition of the movement dependent on both what is seen and what is received from different vehicles in the framework.…”

Section: Interconnected Workmentioning

confidence: 99%

Increase the Performance & Security in VANET using Deduction of Sybil Attack

Naveen¹,

Selvakumar²

2019

Asian Journal of Applied Research

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Vehicular ad-hoc network (VANET) was one of the trending and mostly picked research areas for remote sensing applications due to its features such as low-cost assembly, user-friendly, fault identification, fast acquiring of sensed data, and introducing a wide range of sophisticated tools for remote sensing. The sensed data were evolved as a prominent tool to monitor the real-time scenarios to further process of communication and decision-making. In this paper, we propose an asymmetric encryption algorithm with emphasis on Multi-Variate Quadratic Quasigroups (MVQQ) algorithm and also propose the execution examination of the Sybil attack detection in VANET. The execution metric is taken for the assessment of attack which relies on a packet end to end delay, system throughput, and load. This framework was likewise used to counteract Sybil attack by limiting timestamps given by Roadside Units at a beginning stage itself. An attacker is one of the sorts of end client, yet their role in the system is negative and makes issues for different segments of the system. A serious attack, known as Sybil attack, against ad-hoc networks includes an attacker misguidedly asserting numerous characters. A Sybil attack delivers different messages to different nodes. Every in this paper, we discuss some of the techniques put forwarded by researchers to detect Sybil attack in VANET. In this paper, we have discussed about the loom for detecting Sybil attack in VANET using neighborhood-based method. The simulation setup contains 100 vehicular nodes moving with a consistent speed of 10 m every second. The information rate of vehicular nodes is 10 Mbps with default transmitting intensity of 0.006 watts.

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“…In a Sybil assault the aggressor subverts the notoriety arrangement of a distributed system by making a substantial number of pseudonymous personalities, utilizing them to pick up an excessively huge impact [18]. …”

Section: Sybil Attackmentioning

confidence: 99%

Mitigation of Sybil Attack Using Location Aware Nodes in VANET

2015

IJSR

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VANETs also called as intelligent transportation system (ITS) in which vehicles communicate to provide timely information.Their aim is to provide security, information and management of network. Instead of their many advantages vehicular network is prone to various attacks. In this research removal of Sybil attack in which node creates its multiple identities and it can be affected by various ways. In previous work Sybil attack is prevented by using the timestamp. Every node has some time stamp to communicate with RSU in which identities are verified. If multiple identities exist then there must be an attacker. On high traffic roads there are number of vehicles for which RSU cannot process all vehicles and also vehicles have high mobility due to which timestamp may be collapse or may miss by vehicle. In previous work three methods were used to find the physical measurement of message that were Time of Arrival (TOA), Angel of Arrival (AOA), and Received Signal Strength (RSSI). In this work, we are using GPSR, Which Reduce the Chances of Attacks.

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Modeling Sybil attacker behavior in VANETs

Cited by 8 publications

References 15 publications

Sybil Attack Detection in VANET Using Machine Learning Approach

Sybil Attack Detection in VANET Using Machine Learning Approach

Increase the Performance & Security in VANET using Deduction of Sybil Attack

Mitigation of Sybil Attack Using Location Aware Nodes in VANET

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