Abstract
In today’s era, thinking of Vehicular Ad-hoc Network as a midrib for the leaf of academic, social, corporate, and economic activities will not be erroneous. To avoid any panic situations like road accidents, heavy traffic jams, etc., the timely availability of correct information is obligatory. The presence of malicious nodes within the network will ruin the dream of establishing a safe, secure, and accident-free vehicular network. This objective can be fulfilled only when malicious nodes within the network are identified correctly, and respective actions are taken at the right time. Therefore, there is a great requirement for efficient and intelligent misbehavior detection techniques to deal with such situations. Vehicular networks are very prone to numerous attacks, such as Sybil attacks, unauthorized access, etc. due to their dynamic nature. The main goal of this study is to discuss and bundle various available misbehavior detection schemes and respective solutions to cope with harmful attackers in the network. We have categorized different misbehavior detections on the criteria of architecture, approach, node-centric, and data-centric. The subcategorization is also given within the paper. One section of this paper focuses on the role of machine learning techniques in misbehavior detection as an emerging foot strap for further enhancement. A comparative analysis of various misbehavior detection schemes is also conducted based on performance measures like accuracy, False Positive Rate, Recall, Precision and F-measurement. Finally, the paper concluded by discussing open issues and various research challenges associated with misbehavior detection in the Vehicular Ad-hoc Network.
Similar content being viewed by others
Data Availability
Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.
Code Availability
Code sharing is not applicable to this article.
References
Zaidi, K., & Rajarajan, M. (2015). Vehicular internet: security & privacy challenges and opportunities. Future Internet. https://doi.org/10.3390/fi7030257
Garip, M.T. (2019). Design and mitigation of vehicular botnets in vehicular Ad Hoc networks. Dissertation, University of California
Zaidi, S.K. (2016). Detecting Rogue Nodes in Vehicular Ad–hoc Networks(DETER). Dissertation, University of London
Panjeta, S., Aggarwal, K. (2017). Review paper on different techniques in combination with IDS. International Journal of Engineering Science and Computing, 7, 11623–11630.
Arshad, M., Ullah, Z., Ahmad, N., et al. (2018). A survey of local/cooperative-based malicious information detection techniques in VANETs. EURASIP Journal on Wireless Communications and Networking, 2018, 1–17. https://doi.org/10.1186/s13638-018-1064-y
Kamel, J., Ansari, M., Petit, J., et al. (2020). Simulation framework for misbehavior detection in vehicular networks. IEEE Transactions on Vehicular Technology, 69, 6631–6643. https://doi.org/10.1109/TVT.2020.2984878
Bibmeyer, N. (2014). Misbehavior detection and attacker identification in vehicular Ad hoc networks. Dissertation
Shahid MA, Jaekel A, Ezeife, C. et al (2018) .Review of Potential Security Attacks in VANET. IEEE. https://doi.org/10.1109/MINTC.2018.8363152
Junaid, M., Syed, A., Warip, M. et al (2018) Classification of security attacks in VANET: A review of requirements and perspectives. In MATEC Web of Conferences 150, 06038: 1–7. https://doi.org/10.1051/matecconf/201815006038
Arif, M., Wang, G., Wang, T., et al. (2019). A survey on security attacks in VANETs: Communication. Applications and Challenges. Vehicular Communications., 10, 1111.
Zaidi, T., Faisal, S. (2018). An overview: Various attacks in VANET. ICCCA. https://www.researchgate.net/publication/334760930
Ghaleb, F. A., Maarof, M. A., Zainal, A., et al. (2019). Ensemble-based hybrid context-aware misbehavior detection model for vehicular ad hoc network. Remote Sensing. https://doi.org/10.3390/rs11232852
Ghaleb, F. A., Maarof, M. A., Zainal, A., et al. (2019). Context-aware misbehavior detection scheme for vehicular Ad Hoc networks using sequential analysis of the temporal and spatial correlation of cooperative awareness messages. Vehicular Communications. https://doi.org/10.1016/j.vehcom.2019.100186
Ghaleb, F. A., Saeed, F., Sarem, M. A., et al. (2020). Misbehavior-aware on-demand collaborative intrusion detection system using distributed ensemble learning for VANET. Electronics. https://doi.org/10.3390/electronics9091411
Sedjelmaci, H., & Senouci, S. M. (2015). An accurate and efficient collaborative intrusion detection framework to secure vehicular networks. Computers & Electrical Engineering, 43, 33–47. https://doi.org/10.1016/j.compeleceng.2015.02.018
Hao, Y., Tang, J., & Cheng, Y. (2011). Cooperative sybil attack detection for position based applications in privacy preserved VANETs. IEEE Global Telecommunications Conference-GLOBECOM. https://doi.org/10.1109/GLOCOM.2011.6134242
Zaidi, K., Milojevic, M., Rakocevic, V., et al. (2015). Host-based Intrusion detection for VANETs: A statistical approach to rogue node detection. IEEE Transactions on Vehicular Technology, 65, 6703–6714. https://doi.org/10.1109/TVT.2015.2480244
Theodorakopoulos, G., & Baras, J. S. (2008). Game theoretic modeling of malicious users in collaborative networks. IEEE Journal on Selected Areas in Communications, 26, 1317–1327. https://doi.org/10.1109/JSAC.2008.080928
Guo, F., Wang, Z., Du, S., et al. (2015). Detecting vehicle anomaly in the edge via sensor consistency and frequency characteristic. IEEE Transactions on Vehicular Technology, 68(6), 5618–5628.
Ruj, S., Cavenaghi, M.A., Huang, Z. (2011) On data-centric misbehavior detection in VANETs. In Vehicular technology conference https://doi.org/10.1109/VETECF.2011.6093096
Kerrache, C.A., Lakas, A., Lagraa, N. (2016) Detection of Intelligent Malicious and Selfish Nodes in VANET Using Threshold Adaptive Control. In International conference on electronic devices, systems and applications (ICEDSA).https://doi.org/10.1109/ICEDSA.2016.7818492
Bißmeyer, N., Njeukam, J., Petit, J. (2012) Central misbehavior evaluation for VANETs based on mobility data plausibility. In ACM international workshop on vehicular inter-networking, systems, and applications:73–82. https://doi.org/10.1109/GLOCOM.2011.6134242
Bißmeyer, N., Stresing, C., Bayarou, K.M. (2010) Intrusion detection in VANETs through verification of vehicle movement data. In IEEE vehicular networking conference. 166–173. https://doi.org/10.1109/VNC.2010.5698232
Pooja, B., Pai, M.M., Pai, R.M. (2014) Mitigation of insider and outsider DoS attack against signature based authentication in VANETs. In Asia-Pacific conference on computer aided system engineering (APCASE) 152–157. https://doi.org/10.1109/APCASE.2014.6924490
Lu, H., Li, J., Guizani, M. (2012) A novel ID-based authentication framework with adaptive privacy preservation for VANETs. In Computing, communications and applications conference 345–350. https://doi.org/10.1109/ComComAp.2012.6154869
Abumansoor, O., Boukerche, A. (2011) Towards a secure trust model for vehicular Ad hoc networks services. In IEEE global telecommunications conference-GLOBECOM 2011:1–5. https://doi.org/10.1109/GLOCOM.2011.6134243
Tomandl, A., Fuchs, K.P., Federrath, H. (2014) REST-Net: A dynamic rule-based IDS for VANETs. In IFIP wireless and mobile networking conference (WMNC) 1–8. https://doi.org/10.1109/WMNC.2014.6878854
Liang, L., Ye, H., & Li, Y. G. (2018). Toward intelligent vehicular networks: a machine learning framework. IEEE Internet of Things Journal, 6, 124–135. https://doi.org/10.1109/JIOT.2018.2872122
Erfan, A. S., Rizaner, A., & Ulusoy, A. H. (2018). Trust aware support vector machine intrusion detection and prevention system in vehicular. Computers & Security. https://doi.org/10.1016/j.cose.2018.06.008
Pathan, A. S. K. (2014). The State of Art in Intrusion Prevention and Detection. CRC Press.
Grover, J., Prajapati, N.K., Laxmi, V. et al (2011) Machine learning approach for multiple misbehavior detection in VANET. In International conference on advances in computing and communications :644–653.https://doi.org/10.1007/978-3-642-22720-2_68
So, S., Sharma, P., Petit, J. (2018) Integrating plausibility checks and machine learning for misbehavior detection in VANET. In IEEE international conference on machine learning and applications (icmla):564–571. https://doi.org/10.1109/ICMLA.2018.00091
Ghaleb, F.A., Zainal, A., Rassam, M.A. et al (2017) An effective misbehavior detection model using artificial neural network for vehicular Ad hoc network applications.In IEEE conference on application, information and network security (ains):13–18. https://doi.org/10.1109/AINS.2017.8270417
Faezipour, M., Nourani, M., Saeed, A., et al. (2012). Progress and challenges in intelligent vehicle area networks. Communications of The ACM, 55, 90–100. https://doi.org/10.1145/2076450.2076470
Heijden, R., Dietzel, S., Kargl, F. (2013) Misbehavior detection in vehicular Ad-hoc networks. https://www.researchgate.net/publication/235731503
Ghaleb, F. A., Razzaque, M. A., & Zainal, A. (2014). Mobility pattern based misbehavior detection in vehicular adhoc networks to enhance safety. IEEE. https://doi.org/10.1109/ICCVE.2014.171
Khan, U., Agrawal, S., & Silakari, S. (2015). A detailed survey on misbehavior node detection techniques in vehicular ad hoc networks. Advances in Intelligent Systems and Computing, 339, 11–19. https://doi.org/10.1007/978-81-322-2250-7_2
Singh, P.K., Gupta, S., Vashistha, R. (2019) Machine learning based approach to detect position falsification attack in VANETs. In Communications in Computer and Information Science, 939. https://doi.org/10.1007/978-981-13-7561-3_13
Montenegro, J., Iza, C., Igartua, M.A. (2020). Detection of position falsification attacks in VANETs applying trust model and machine learning. In the 17th ACM Symposium on Performance Evaluation f Wireless Ad Hoc, Sensor & UbiquiousNetworks, 2020, 9–16. https://doi.org/10.1145/3416011.3424757
Erskine, S. K., & Elleithy, K. M. (2019). Real-time detection of DoS attacks in IEEE 80211p using fog computing for a secure intelligent vehicular network. Electronics. https://doi.org/10.3390/electronics8070776
Lahrouni, Y., Pereira, C., Bensaber, B.A. et al (2017) Using mathematical methods against denial of service (DoS) attacks in VANET. In the 15th ACM International Symposium on Mobility Management nd Wireless Access, 2017, 17–22. https://doi.org/10.1145/3132062.3132065
Pattanayak, B., Pattnaik, O., & Pani, S. (2020). Dealing with Sybil Attack in VANET. Intelligent and Cloud Computing, 1, 471–480.
Lachdhaf, S., Mazouzi, M., & Abid, M. (2018). Secured AODV routing protocol for the detection and prevention of black hole attack in VANET. Advanced Computing: An International Journal. https://doi.org/10.5121/acij.2018.9101
Kosmanos, D., Pappas, A., & Maglaras, L. (2020). A novel Intrusion Detection System against spoofing attacks in connected electric Vehicles. Array. https://doi.org/10.1016/j.array.2019.100013
Dai, C., Xiao, X., Ding, Y. et al (2018) Learning based security for VANET with blockchain. In IEEE internation conference oncommunication systems. https://doi.org/10.1109/ICCS.2018.8689228
Albouq, S. S., Fredericks, E. M. (2017) Detection and avoidance of wormhole attacks in connected vehicles. In the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, 2017, 107–116. https://doi.org/10.1145/3132340.3132346
Chbib, F., Fahs, W., Haydar, J. (2020) Message Fabrication Detection Model based on Reactive Protocols in VANET. In IEEE. https://doi.org/10.1109/CSNet50428.2020.9265458
Das, A. K., Kalam, S., Sahar, N., & Sinha, D. (2020). UCFL: User categorization using fuzzy logic towards PUF based two-phase authentication of fog assisted IoT devices. Computers & Security. https://doi.org/10.1016/j.cose.2020.101938
Tabassum, A., Sadaf, S., & Sinha, D. (2020). Secure anti-void energy-efficient routing (SAVEER) protocol for WSN-based iot network. Advances in Computer Intelligence. https://doi.org/10.1007/978-981-13-8222-2-11
Funding
This research received no external funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sangwan, A., Sangwan, A. & Singh, R.P. A Classification of Misbehavior Detection Schemes for VANETs: A Survey. Wireless Pers Commun 129, 285–322 (2023). https://doi.org/10.1007/s11277-022-10098-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-022-10098-1