Position forging attacks in Vehicular Ad Hoc Networks: Implementation, impact and detection

Grover, Jyoti; Gaur, Manoj Singh; Laxmi, Vijay

doi:10.1109/iwcmc.2011.5982632

Cited by 15 publications

(10 citation statements)

References 16 publications

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“…Most of the possible attacks in VANET are performed through manipulating context information [51,52]. In addition, attackers in VANET target this type of information as they can inflict major damage easily while they remain undetected [23,25,26,53]. As such, this study aims to build a mobility information-based MDS.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, attackers can hijack and control the compromised vehicles and use them to commit many crimes, including terrorist attacks, assassination, and running over crowds of people, to name a few. Several studies have investigated the influences of misbehavior on VANET performance [21][22][23]. These studies revealed that attackers could significantly degrade the traffic flow, the performance of the routing protocol, and channel utilization.…”

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 The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles.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

confidence: 99%

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confidence: 99%

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

et al. 2019

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“…MANET, as the VANET distinguishes MANET in terms of the following features: -such as the high-speed mobility ofhybrid communication pattern, self-organizing nature of the network It does now not rely on any constant community infrastructure. [12], [1]4, [18] …”

Section: Motivationmentioning

confidence: 99%

Performance Evaluation of Distance based and Speed based Beaconing in VANET

Kanwar¹,

Parveen²

2017

IJCA

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The Vehicular Ad-hoc network (VANET), is a technology that uses moves cars as nodes in a network to create a mobile network. VANET turns every participating car into a wireless node, allowing cars just about 100 to 300 meters of each other to connect and, in turn, create a network with a wide range. However, in situations where nodes are movable or when nodes often switch on and off, the local topology rarely remains fixed. Hence, it is necessary that each node broadcasts its updated location information to all of its neighbours. These location update packets are usually referred to as beacons. Beacons are broadcast periodically for maintaining an accurate neighbour list. In this paper performance evaluation is done on distance based and speed based beaconing schemes, considering the performance metrics such as average delay, total packets dropped, maximum delay and minimum delay.

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“…In our previous work, we have introduced variants of position forging attacks and their impact on VANET performance. Proposed work is an extended version of our [30] previous work.…”

Section: Related Workmentioning

confidence: 99%

Attack models and infrastructure supported detection mechanisms for position forging attacks in vehicular ad hoc networks

Grover

Laxmi

Gaur

2013

CSIT

Self Cite

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Vehicular ad hoc networks (VANETs) are vulnerable to message forging attacks, where an attacker creates a new message or replays/modifies an existing message. Forging of message can be carried out by attacker directly or indirectly through another vehicle. In VANETs, each vehicle periodically broadcasts short packets (beacons) with its identifier, time and current geographical position. In a position forging attack, an attacker broadcasts timely coordinated traffic warning messages with forged positions, producing illusion of an accident/traffic jam or an emergency braking. In this manner, VANET performance degrades in terms of channel utilization. It also severely impact the performance of security algorithms. In this paper, our focus is on the design and implementation of various forms of position forging attacks. We have presented detection approaches for these attacks. Unlike existing detection approaches, our methods are not based on the concept of estimating the position of senders. We have analyzed the impact of forged position information on average vehicle speed, number of collisions and percentage of delivered packets. Effectiveness of detection methods for various attack scenarios is compared. Instead, it works on the pattern of position information broadcast in consecutive packets. Simulation results demonstrates the effectiveness and adaptability of our proposed approach for VANETs.

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Position forging attacks in Vehicular Ad Hoc Networks: Implementation, impact and detection

Cited by 15 publications

References 16 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

Performance Evaluation of Distance based and Speed based Beaconing in VANET

Attack models and infrastructure supported detection mechanisms for position forging attacks in vehicular ad hoc networks

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