Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks

Gu, Pengwenlong; Khatoun, Rida; Begriche, Youcef; Serhrouchni, Ahmed

doi:10.1109/wcnc.2017.7925783

Cited by 47 publications

(26 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, ROPE protocol [13] will be able to improve the drawbacks realised in SeR-Loc protocol though it increases the cost of the system with increase in hardware requirement. Similarly, SeR-Loc protocol demonstrates requirements like identification of the deployment density of the nodes which are not apt for securing the system against attacks [14].…”

Section: Hirloc Protocol Faces the Issue Of Computing Complexity And High Communication Overheadmentioning

confidence: 99%

Detection of Localization Error in a WSN under Sybil Attack using Advanced DV-Hop Methodology

Suma¹

2021

JSWS

View full text Add to dashboard Cite

Localization is one of the most important aspects of Wireless Sensor Networks that make it applicable in a number of fields and areas. WSN advances in the technological aspects the number of attacks on the nodes of the WSN have also increased proficiently resulting in a number of security issues. One such attack is the Sybil attack which uses multiple pseudonymous identities to disrupt the reputation of the system. This paper is used to analyse the Sybil attacks using a detection and defence algorithm based on distance vector hop. Simulation of the results using the algorithm will be useful in effectively enhancing security of WSN nodes. In this proposed work based on the experimental analysis we have found out that with 50 beacon nodes, we have been able to decrease the average localisation error buy a solid 4% when compared with previous methodologies.

show abstract

Section: Hirloc Protocol Faces the Issue Of Computing Complexity And High Communication Overheadmentioning

confidence: 99%

Detection of Localization Error in a WSN under Sybil Attack using Advanced DV-Hop Methodology

Suma¹

2021

JSWS

View full text Add to dashboard Cite

show abstract

“…Furthermore, Shi [30] was able to detect attacks in the initialization phase when combining the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol features with RSSI-ID tables. Still in WSN, Gu [31,32] used the K-nest and SVM algorithm in RSSI to achieve high detection efficiency and low error detection rate. However, the algorithm consumes too much detection time.…”

Section: Related Workmentioning

confidence: 99%

Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information

Wang

Zhu

Gong

et al. 2018

Sensors

View full text Add to dashboard Cite

With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.

show abstract

“…Recently, Sybil attacks have been studied in the context of Vehicular Ad-hoc Networks (VANETs) [7,11,12]. In [7], the authors built an event-based reputation system that feeds a trust management system that restricts the dissemination of false messages.…”

Section: Managing Sybil Attacks and Collusionsmentioning

confidence: 99%

“…In [7], the authors built an event-based reputation system that feeds a trust management system that restricts the dissemination of false messages. In [11,12], the authors use driving patterns of vehicles and detect Sybils using classifiers, such as minimum distance classifier and support vector machines. In location-based social networks, [15] states that it is not normal the appearance of continuous gatherings, and use the detection of these events to identify Sybils.…”

Section: Managing Sybil Attacks and Collusionsmentioning

confidence: 99%

Crowdsourcing Under Attack: Detecting Malicious Behaviors in Waze

Sánchez

Rosas

Hidalgo

2018

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Social networks that use geolocalization enable receiving data from users in order to provide information based on their collective experience. Specifically, this article is interested in the social network Waze, a real-time navigation application for drivers. This application uses methods for identifying users that are open and free, where people are able to hide their identity by using a pseudonym. In this context, malicious behaviors can emerge, endangering the quality of the reports on which the application is based. We propose a method to detect malicious behavior on Waze, which crawls information from the application, aggregates it and models the data relationships in graphs. Using this model the data is analyzed according to the size of the graph: for large interaction graphs, we use a Sybil detection technique, while for small graphs we propose the use of a threshold-based mechanism to detect targeted behaviors. The results show that it is complex to use the large-scale Sybil attack detection techniques due to parameter tuning. However, good success rates can be achieved to tag users as honest and malicious if there are a small number of interactions between these groups of users. On the other hand, for small graphs, a straightforward analysis can be performed, since the graphs are sparse and the users have a limited number of connections between them, making clear the presence of outliers.

show abstract

Support Vector Machine (SVM) Based Sybil Attack Detection in Vehicular Networks

Cited by 47 publications

References 21 publications

Detection of Localization Error in a WSN under Sybil Attack using Advanced DV-Hop Methodology

Detection of Localization Error in a WSN under Sybil Attack using Advanced DV-Hop Methodology

Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information

Crowdsourcing Under Attack: Detecting Malicious Behaviors in Waze

Contact Info

Product

Resources

About