International Journal of Computer Networking, Wireless and Mobile Communications (IJCNWMC) ISSN 2250-1568 Vol. 3, Issue 3, Aug 2013, 51-56 © TJPRC Pvt. Ltd.
EXPOSITORY STUDY ON SYBIL ATTACK COUNTER MEASURES IN VANET JIS MARY JACOB & ANITA JOHN Department of Computer Science & Engineering, Rajagiri School of Engineering & Technology, Rajagiri Valley, Kochi, Kerala, India
ABSTRACT Vehicular Ad hoc Networks (VANET) are being advocated as a mode to augment road safety and driving comfort. Regrettably, in VANETs most privacy safeguarding schemes are susceptible to Sybil attacks, wherein a malicious user can pretend to be multiple vehicles by forging identities of multiple vehicles. Privacy and security are two major concerns in VANETs. Vehicular communication plays a significant role in providing safe transportation by means of safety message exchange. An expository study of sybil attack counter measures is presented in this paper.
KEYWORDS: VANET, Beacon, Cryptography, Expository, Footprint, Privacy, Pseudonym, Security, Sybil Attack, Timestamp, Trajectory
INTRODUCTION Vehicular Ad-Hoc Network (VANET) is a specific type of Mobile Ad-Hoc Network (MANET) that provides communication between nearby vehicles and between vehicles and nearby RSUs (Road Side Units). The brainwave of vehicular ad hoc networks includes the frequent exchange of data by vehicles (nodes) to aid road safety and route planning. For illustration, vehicles can cooperatively sense information about traffic congestion and relay them to other vehicles or control stations to facilitate traffic re-routing. Many applications can turn out to be realistic if vehicles team up to achieve a common goal. In the safety related applications, each vehicle periodically broadcasts its geographic information (generated by a global positioning system (GPS) device) including its current position, direction and velocity as well as road information every 300ms for the sake of collision avoidance . To ensure the authenticity of messages propagated in VANET, a straight-forward method is to use public keys certified by a Certification Authority (CA). The certified public keys are called “pseudonyms” . To prevent vehicles from being tracked by identifying the keys that are used, each vehicle can switch among multiple pseudonyms that are difficult to correlate to each other. With this approach it is difficult for an attacker to identify vehicles by examining the used keys. This scheme has been proposed by many researchers and works efficiently . Sybil attack is an important security concern for position based applications in VANET . In certain cooperative scheme based applications, a malicious user (vehicle) may forge several pseudonymous entities and claim that they are at certain positions to compromise the overall performance of application .In this paper, we perform an expository study on various methods to counter Sybil attack in VANETs.
RELATED WORK A privacy-preserving scheme  to detect Sybil attacks in VANETs has been proposed under a commonly used framework in the existing network . The framework assumes that vehicles communicate with each other in a multihop manner, and the communication is monitored by a RSB (Road Side Box) through passive overhearing. The RSB is securely connected to the DMV (Department of Motor Vehicle) via a backhaul wired network. The DMV plays the role of a certificate authority (CA) and has the ability to manage vehicle registration, ownership and other administrative policies.
Jis Mary Jacob & Anita John
This scheme requires the DMV to provide vehicles with a pool of pseudonyms. To prevent sybil attack, the pseudonyms are assigned to a particular vehicles are hashed to a common value. By calculating hashed values of the overheard pseudonyms, an RSB and the DMV will be able to determine whether the pseudonyms came from the same pool, thus helping to identify a Sybil attack. In this scheme, privacy is preserved as long as the RSB can be trusted. DMV is the trusted party that maintains vehicle records and distributes certified pseudonyms to vehicles. RSBs are wireless access points. They are scattered along the road and connected to DMV. The RSBs monitor vehicular activity, identify suspicious behaviour and report to the DMV. This protocol can recognize smart attackers who may adjust their communication range. Furthermore, a more complicated scenario in which several legal vehicles may conspire with each other will also be discussed. In timestamp related protocols , Sybil attack is detected based on the fact that different vehicles barely pass the same sequence of RSUs at the same time. On the contrary, accord among malicious vehicles may compromise this kind of protocols. Regardless of great potential of VANET applications, security has long been a concern and thus it is imperative to provide functionality to validate an event reported by vehicles in all types of applications . Although the IEEE 1609.2  standard is proposed to secure VANETs using digital signatures and certificates to prevent attacks, the standard fails to address event falsification. For instance, a selfish driver can engender a false alert about congestion on a road segment, but other drivers will believe the alert since it is digitally signed using valid credentials. As an upshot, these drivers will avoid this road, providing the selfish driver with an improved driving experience. Counting the number of vehicles that report an event allows a recipient to evaluate the validity of a VANET event  . The sybil attack was first described and formalized by for peer to peer networks by Douceur . In the paper, the author proposed methods to detect the attack by exploiting limitations of the attacker in three different resources: communication speed, computation speed and storage capacity. Though, computation speed and storage capacity are not bottlenecks in VANETs, whilst communication test may incur extra overhead. Using extra hardware is another way to detect sybil attacks, such as laser , radar . The drawbacks of these methods are as follows. First of all, additional hardware means higher costs. Secondly, laser or radar waves can only be disseminated in straight lines. It is difficult to detect vehicles at turn. Another technique exploits directional antenna to identify position/direction from which a message arrives . A vehicle launching a Sybil attack is expected to get caught because all duplicate messages will come from same position. However, in dense networks, localization errors can lead to frequent false positives. Leinmuller describe the effect of false position information in VANETs . The authors show that the effect of malicious nodes is more severe in highway scenario than in city scenarios. In their simulations, the delivery ratio decreases by 90% to 100% if malicious nodes simply drop messages. Radio signal measurement method determines the attack based in strength of the received signal . The essence of this method is that the distance between sending node and receiving node can be calculated from the receiving side under the assumption that all vehicles send messages with the same transmitting power. If a vehicleâ€™s position does not match its power level, it will be identified as a sybil node. Although signal measurement does not need any extra infrastructure, the distance between the sender and receiver node is hard to be precisely estimated.
EXPOSITORY STUDY Security Analysis In the VANETs, vehicles communicate with each other through radio over the IEEE 802.11p on 5.9GHz . Among seven communication channels, there is one control channel for management data, short message exchange and an accident avoidance channel for safety message broadcasting. It is assumed that all vehicles broadcast their geographic
Expository Study on Sybil Attack Counter Measures in VANET
information periodically in the accident avoidance channel with the same communication range. Vehicles are required to record all their neighbours’ geographic information for the previous 30 seconds. Most security protocols merely provide location privacy for vehicles under the condition that eavesdroppers can only hear from them intermittently. Many researchers ,  proposed public key cryptography to defend this attack. In this scenario, a central authority is responsible for issuing certificate to each vehicle. Certificate includes a set of physical attributes of vehicle and public key information. The whole information is recorded by central authority. Signal strength based position verification scheme was proposed by Xiao . It acquires the advantage of VANET traffic pattern and roadside base station. The vital idea in this scheme is to estimate a node’s position by analyzing its signal strength distribution and confirm whether its claim position is consistent with the estimated position. In this scheme each node plays three roles termed as claimer, witness and verifier. Claimer that broadcast beacon messages for the purpose of discovering its neighbours. Beacon message contain node’s identity and its GPS position. Witness is the neighbouring node within signal range that stores the corresponding information in their memory. Verifier confirms the claimed position of the vehicle by gathering information from its witness. If the estimated position is far different from the claimed position, it entails the existence of a sybil node. Once a sybil node is detected, the sybil classification algorithm is executed to check other sybil nodes generated by same attacker. Detection using neighbouring vehicle was proposed in . In this approach every node participates to detect the suspect node in the network. Every vehicle have different group of neighbours at different time interval. A vehicle send beacon message to ensure its presence and alert messages to guarantee the safety of vehicle. Every vehicle after collecting enough beacon packets from neighbouring nodes, make a record of group of neighbours at regular interval of time. After considerable duration of time these nodes further exchange their packets with nodes within its range. After sharing their tables they check their neighbouring table, if some nodes are simultaneously observed with same set of neighbours at different interval of time, then these nodes are under sybil attack. A novel mechanism for sybil attack detection  footprint using the trajectories of vehicle for identification while still preserving location privacy of vehicles. In footprint, location hidden authorized message generation scheme is used.
Performance Analysis An analysis of the Sybil attack counter measures in VANET is tabulated below. Table 1: Performance Analysis of Sybil Attack Counter Measures Algorithm Cooperative Sybil attack detection in location based privacy preserved network
Description This scheme identify sybil attacks locally in a cooperative way by examining the rationality of vehicles' position to their own neighbours
Limitation This would not work in multimalicious and multi- sybil node scenario. Additional computations are required to calculate the neighbouring information due to highly dynamic topology of VANET.
Detection using neighbouring vehicle
In this scheme every node participates to detect the suspect node. If a vehicle has same set of neighbours at different interval of time, then it is a suspect.
Privacy Preserving Detection Scheme
Privacy is preserved while detecting sybil attack in network. In this approach, the Department of Motor Vehicles (DMV) provide a pool of pseudonyms for every vehicle and every vehicle pseudonyms are hashed to a common value. Hash values are stored in RSU (Road Side Unit) and DMV.
In this every vehicle needs to register itself to trusted authorities and it is very knotty to implement as there are large number of vehicles.
Public Key Cryptography
A central authority is responsible for giving certificate to each vehicle. Every vehicle uses certificate to authenticate itself.
Vehicular public key infrastructure is very hard to deploy due to large number of vehicles by different manufacturers.
Jis Mary Jacob & Anita John
Radio Resource Testing
Resource Testing 
Table 1: Contd., Based on the assumption that each physical entity has only one radio resource and can transmit or receive only on one channel. This is based on the assumption that each physical entity has limited number of resources.
Signal Strength based Position Verification Scheme
In this a nodeâ€™s position is estimated by analyzing its signal strength distribution and verify whether its claimed position is consistent with the estimated position.
Sybil attack detection using footprint mechanism
This is based on footprint mechanism where trajectories of vehicles are used for identification while still preserving location privacy. To be uniquely identified, vehicle collects a series of authorized messages as it keeps travelling. Sequence of authorized messages constitutes trajectory of the vehicle.
Timestamp related approach
In this approach, only RSU issues timestamp to vehicle when it passes nearby and each timestamp is digitally is signed by RSU.
It is difficult to be achieved in VANET due to high mobility. The attacker can have more computational resources than candid nodes. The attacker is clever enough to increase the signal strength while sending beacon message with its wrong position. Signal strength based verification accuracy is limited with an error about 10m. If two entities are very close, we cannot make a clear distinction between neighbouring nodes and sybil nodes. In footprint, it is assumed that all RSUs are trustworthy. However if an RSU is compromised, it can help a malicious vehicle to fake legal trajectories. In that case footprint cannot detect such trajectories. This approach does not work in complex roadways where two vehicles coming from opposite sides, this may result in false detection as both vehicles may receive same series of certificate from same RSU for some significant period of time . If this time exceeds observation period, nodes will be erroneously detected as sybil nodes.
In sybil attack, a vehicle forges the identities of multiple vehicles and these identities can be used to play any type of attack in the system. An illusion of presence of additional vehicles is created by these bogus identities. Sybil attack can act as a catalyst to any type of attack. Another problem of radio resource testing is that the attacker can employ multiple radio devices at once. Public Key Cryptography consumes large memory and is also time consuming. Signal strength based position verification approach has limited accuracy. Timestamp series has got challenges, for instance the sybil attack detection may be difficult if RSUs are located at intersections.
CONCLUSIONS In this paper, we have presented an outline of diverse approaches proposed by various researchers to shield sybil attack in VANETs. The sybil attack is perilous as it creates an illusion of traffic congestion and possesses the potential to instill false data in the network by means of fabricated non-existing vehicles. Sybil attacks can also instigate denial of service (DOS) attack by sending immense amount of bogus information and evade the true information from reaching the destination. It may lead to accident or force the driver to take erroneous decisions. The resolution proposed by various researchers possesses some fissure within them. Some schemes are difficult to implement in VANET due to its dynamic topology. Among the above discussed techniques, timestamp approach, sybil attack detection using footprint mechanism and neighbouring vehicles parameter seems to be better solution
Expository Study on Sybil Attack Counter Measures in VANET
Road safety impinge on the life of people, many people are unnerved due to road accidents yearly around the globe. As a result, many industries invest a lot in enhancement of road safety. Consequently, VANET is one of the most promising areas that studies communication among vehicles. Presently, we are deficient in an efficient and broad-spectrum solution that scales well to large system. There exist numerous solutions that can limit or thwart the attack in several individual application domains.
Maxim Raya and Jean-Pierre Hubaux. 2007. Securing vehicular ad hoc networks. J. Comput. Secur. 15, 1 (January 2007), 39-68.
Tong Zhou; Choudhury, R.R.; Peng Ning; Chakrabarty, K, "P2DAP â€” Sybil Attacks Detection in Vehicular Ad Hoc Networks," Selected Areas in Communications, IEEE Journal on , vol.29, no.3, pp.582,594, March 2011, doi: 10.1109/JSAC.2011.110308
Stefan Rass, Simone Fuchs, Martin Schaffer, and Kyandoghere Kyamakya. 2008. How to protect privacy in floating car data systems. In Proceedings of the fifth ACM international workshop on VehiculAr InterNETworking (VANET '08). ACM, New York, NY, USA, 17-22. DOI=10.1145/1410043.1410047
Giorgio Calandriello, Panos Papadimitratos, Jean-Pierre Hubaux, and Antonio Lioy. 2007. Efficient and robust pseudonymous authentication in VANET. In Proceedings of the fourth ACM international workshop on Vehicular ad hoc networks (VANET '07). ACM, New York, NY, USA, 19-28. DOI=10.1145/1287748.1287752.
John R. Douceur. 2002. The Sybil Attack. In Revised Papers from the First International Workshop on Peer-toPeer Systems (IPTPS '01), Peter Druschel, M. Frans Kaashoek, and Antony I. T. Rowstron (Eds.). SpringerVerlag, London, UK, UK, 251-260.
Chenhui Hu, Xinbing Wang, Ding Nie, and Jun Zhao. 2010. Multicast scaling laws with hierarchical cooperation. In Proceedings of the 29th conference on Information communications (INFOCOM'10). IEEE Press, Piscataway, NJ, USA, 1801-1809.
Luoyi Fu; Yi Qin; Xinbing Wang; Xue Liu, "Converge-cast with MIMO," INFOCOM, 2011 Proceedings IEEE , vol., no., pp.649,657, 10-15,April,2011,doi: 10.1109/INFCOM.2011.5935244.
Soyoung Park; Aslam, B.; Turgut, D.; Zou, C.C., "Defense against Sybil attack in vehicular ad hoc network based on roadside unit support," Military Communications Conference, 2009. MILCOM 2009. IEEE , vol., no., pp.1,7, 18-21 Oct. 2009,doi: 10.1109/MILCOM.2009.5379844.
Tao Yang; Lingbo Kong; Wei Xin; Jianbin Hu; Zhong Chen, "Resisting relay attacks on vehicular Passive Keyless Entry and start systems," Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on , vol., no., pp.2232,2236, 29-31 May 2012,doi: 10.1109/FSKD.2012.62341559.
10. IEEE Trial-Use Standard for Wireless Access in Vehicular Environments - Security Services for Applications and Management Messages," IEEE Std 1609.2-2006 , vol., no., pp.0_1,105, 2006 doi: 10.1109/IEEESTD.2006.243731. 11. Philippe Golle, Dan Greene, and Jessica Staddon. 2004. Detecting and correcting malicious data in VANETs. In Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks (VANET '04). ACM, New York, NY, USA, 29-37. DOI=10.1145/1023875.1023881.
Jis Mary Jacob & Anita John
12. Tiffany Hyun-Jin Kim, Ahren Studer, Rituik Dubey, Xin Zhang, Adrian Perrig, Fan Bai, Bhargav Bellur, and Aravind Iyer. 2010. VANET alert endorsement using multi-source filters. In Proceedings of the seventh ACM international workshop on VehiculAr InterNETworking (VANET '10). ACM, New York, NY, USA, 51-60. DOI=10.1145/1860058.1860067. 13. Maxim Raya and Jean-Pierre Hubaux. 2005. The security of vehicular ad hoc networks. In Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks (SASN '05). ACM, New York, NY, USA, 11-21. DOI=10.1145/1102219.1102223. 14. K. Ibrahim, M. C. Weigle and G. Yan, “Light-weight laser aided position verification for CASCADE,” In Proceedings of International Conference on WAVE, Deaborn, MI, Dec.2008. 15. Gongjun Yan Yan, Gyanesh Choudhary, Michele C. Weigle, and Stephan Olariu. 2007. Providing VANET security through active position detection. In Proceedings of the fourth ACM international workshop on Vehicular ad hoc networks (VANET '07). ACM, New York, NY, USA, 73-74. DOI=10.1145/1287748.1287762. 16. T. Leinmuller, E. Schoch, “Greedy routing in highway scenarios: the impact of position faking nodes,” In Proc. of the Workshop on Intelligent Transportation(WIT),2006. 17. Jyoti Grover, Manoj Singh Gaur, and Vijay Laxmi. 2010. A novel defense mechanism against sybil attacks in VANET. In Proceedings of the 3rd international conference on Security of information and networks (SIN '10). ACM, New York, NY, USA, 249-255. DOI=10.1145/1854099.1854150. 18. Bin Xiao, Bo Yu, and Chuanshan Gao. 2006. Detection and localization of sybil nodes in VANETs. In Proceedings of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks (DIWANS '06). ACM, New York, NY, USA, 1-8. DOI=10.1145/1160972.1160974. 19. Jyoti Grover, Manoj Singh Gaur, Vijay Laxmi, and Nitesh Kumar Prajapati. 2011. A sybil attack detection approach using neighboring vehicles in VANET. In Proceedings of the 4th international conference on Security of information and networks (SIN '11). ACM, New York, NY, USA, 151-158. DOI=10.1145/2070425.2070450. 20. Shan Chang, Yong Qi, Hongzi Zhu, Jizhong Zhao, Xuemin (Sherman) Shen, "Footprint: Detecting Sybil Attacks in Urban Vehicular Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 6, pp. 11031114, June 2012, doi:10.1109/TPDS.2011.263. 21. Yong Hao; Jin Tang; Yu Cheng, "Cooperative Sybil Attack Detection for Position Based Applications in Privacy Preserved VANETs," Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE , vol., no., pp.1,5, 5-9 Dec. 2011, doi: 10.1109/GLOCOM.2011.6134242.
Vehicular Ad hoc Networks (VANET) are being advocated as a mode to augment road safety and driving comfort. Regrettably, in VANETs most priv...