An Optimisation driven Deep Residual Network for Sybil attack detection with reputation and trust-based misbehaviour detection in VANET

References

• Abdulkader, Z. A., Abdullah, A., Abdullah, M. T., & Zukarnain, Z. A. (2018). A survey on Sybil attack detection in vehicular Ad hoc networks (VANET). Journal of Computers, 29(2), 1–6 DOI: 10.3966/199115992018042902001.
   Google Scholar
• Abhishek, N. V., Lim, T. J., Sikdar, B., & Liang, B. (2019).Detecting RSU misbehavior in vehicular edge computing. In IEEE/CIC International Conference on Communications in China (ICCC), 42–47.
   Google Scholar
• Chen, Z., He, M., Liang, W., & Chen, K. (2015). Trust-aware and low energy consumption security topology protocol of wireless sensor network. Journal of Sensors 2015 1–10 doi:10.1155/2015/716468 .
   Web of Science ®Google Scholar
• Chena, Z., Chena, Y., Wua, L., Chenga, S., & Lina, P. (2019). Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions. Energy Conversion and Management, 198, 111793. https://doi.org/10.1016/j.enconman.2019.111793
   Web of Science ®Google Scholar
• Cheng, R., & Jin, Y. (2014). A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics, 45(2), 191–204. https://doi.org/10.1109/TCYB.2014.2322602
   PubMed Web of Science ®Google Scholar
• Das, A., & Islam, M. M. (2011). SecuredTrust: A dynamic trust computation model for secured communication in multiagent systems. IEEE Transactions on Dependable and Secure Computing, 9(2), 261–274. https://doi.org/10.1109/TDSC.2011.57
   Web of Science ®Google Scholar
• Desogus, M., & Casu, E. (2019). A diagnostic approach to corporate sustainability based on normalized net margins and extended present value. International Journal of Financial Management, 9(2), 8–16. https://www.proquest.com/openview/0474f89da73513663e5e5dfb2cbbe952/1?pq-origsite=gscholar&cbl=2030939).
   Google Scholar
• Desogus, M., & Casu, E. (2020). On the monetary causes of inequality: A review of the literature and an alternate way forward. International Journal of Economics, Finance and Management Sciences, 8(4), 156–167. https://doi.org/10.11648/j.ijefm.20200804.14
   Google Scholar
• Desogus, M., & Venturi, B. (2019). Bank crashes and micro enterprise loans. International Journal of Business and Social Science, 10(12), 35–53. https://doi.org/10.30845/ijbss.v10n12a4
   Google Scholar
• Douceur, J. R. (2002). The Sybil attack. In International workshop on peer-to-peer systems Springer (pp. 251–260). Springer.
   Google Scholar
• Faisal, S. M., & Zaidi, T. (2020). Timestamp based detection of Sybil attack in VANET. IJ Network Security, 22(3), 397–408 doi: 10.6633/IJNS.202005.
   Google Scholar
• Funderburg, L. E., & Lee, I. Y. (2021). A privacy-preserving key management scheme with support for SYBIL attack detection in VANETs. Sensors, 24(4), 1–17. https://doi.org/10.3390/s21041063
   Google Scholar
• Gangappa, M., Mai, C. K., & Sammulal, P. (2019). Enhanced crow search optimization algorithm and hybrid NN-CNN classifiers for classification of land cover images. Multimedia Research, 2(3), 12–22. https://doi.org/10.46253/j.mr.v2i3.a2.
   Google Scholar
• Getaneh, S. T. (2019).Enhanced Security Mechanism to Detect Sybil Attacks in VANETs. Doctoral dissertation.
   Google Scholar
• Ghaleb, F. A., Maarof, M. A., Zainal, A., Rassam, M. A., Saeed, F., & Alsaedi, M. (2019). Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages. Vehicular Communications, 20, 100186. https://doi.org/10.1016/j.vehcom.2019.100186
   Web of Science ®Google Scholar
• Hamed, H., Haddad, A. K., & Haghighi, S. G. (2018).Sybil attack detection in urban VANETs based on RSU support. In Electrical Engineering (ICEE), Iranian Conference, 602–606.
   Google Scholar
• Hasrouny, H., Samhat, A. E., Bassil, C., & Laouiti, A. (2017). VANET security challenges and solutions: A survey. Vehicular Communications, 7, 7–20. https://doi.org/10.1016/j.vehcom.2017.01.002
   Web of Science ®Google Scholar
• Kamel, J., Wolf, M., Heijiden, R. W. V. D., Urien, P., & Kargl, F. (2020).VeReMi extension: A dataset for comparable evaluation of misbehavior detection in VANETs. In ICC 2020-2020 IEEE International Conference on Communications (ICC), 1–6.
   Google Scholar
• Kaur, R., Singh, T. P., & Khajuria, V. (2018).Security issues in vehicular ad-hoc network (VANET). In IEEE 2nd International conference on trends in Electronics and Informatics (ICOEI), 884–889.
   Google Scholar
• Kaveh, A., & Farhoudi, N. (2016). Dolphin echolocation optimization: Continuous search space. Advances in Computational Design, 1(2), 175–194. https://doi.org/10.12989/acd.2016.1.2.175
   Google Scholar
• KDD Cup 1999 data taken from. April 2021. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
   Google Scholar
• Malik, N. (2020). Enery-aware routing in MANET: Hybrid genetic and group search algorithm. Journal of Networking and Communication Systems, 3(4 35–41 https://doi.org/10.46253/jnacs.v3i4.a5).
   Google Scholar
• Muhamad, A., & Elhadef, M. (2018). Sybil attacks in intelligent vehicular ad hoc networks: A review. Advanced Multimedia and Ubiquitous Engineering 518 , 547–555.
   Google Scholar
• Parham, M., & Pouyan, A. A. (2020). An effective privacy-aware Sybil attack detection scheme for secure communication in vehicular ad hoc network. Wireless Personal Communications, 113(2), 1149–1182. https://doi.org/10.1007/s11277-020-07272-8
   Web of Science ®Google Scholar
• Quevedo, C. H. O. O., Quevedo, A. M. B. C., Campos, G. A., Gomes, R. L., & Celestino, J. (2020).An intelligent mechanism for Sybil attacks detection in VANETs. In ICC International Conference on Communications (ICC), 1–6.
   Google Scholar
• Rakhi, S., & Shobha, K. R. (2019).Performance analysis of an efficient data-centric misbehavior detection technique for vehicular networks. In International Conference on Computer Networks and Communication Technologies, 321–331.
   Google Scholar
• Rewadkar, D., & Dharmpal, D. (2018). FGWSO‐TAR: Fractional glowworm swarm optimization for traffic aware routing in urban VANET. International Journal of Communication Systems, 31(1), e3430. https://doi.org/10.1002/dac.3430
   Web of Science ®Google Scholar
• Rupapara, V., Narra, M., Gonda, N. K., & Thipparthy, K. (2020).Relevant data node extraction: A web data extraction method for non contagious data. In The proceeding of 5th International Conference on Communication and Electronics Systems (ICCES), IEEE, 500–505.
   Google Scholar
• Rupapara, V., Rustam, F., Shahzad, H. F., Mehmood, A., Ashraf, I., & Choi, G. S. (2021). Impact of SMOTE on imbalanced text features for toxic comments classification using RVVC model. IEEE Access, 9, 78621–78634. https://doi.org/10.1109/ACCESS.2021.3083638
   Web of Science ®Google Scholar
• Sakiz, F., & Sen, S. (2017). A survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV. Ad Hoc Networks, 61, 33–50. https://doi.org/10.1016/j.adhoc.2017.03.006
   Web of Science ®Google Scholar
• Suganya, R. (2018). An end to end security and protection for against attack in vehicular Ad-Hoc network. International Journal of Pure and Applied Mathematics, 119(18), 1359–1370. https://acadpubl.eu/hub/2018-119-18/2/101.pdf.
   Google Scholar
• Veeraiah and Krishna. (2018). Intrusion detection based On Piecewise Fuzzy C-Means clustering and fuzzy naïve Bayes rule. Multimedia Research, 1(1), 27–32. https://doi.org/10.46253/j.mr.v1i1.a4.
   Google Scholar
• Vegni, A., Loscrì, V., & Petrolo, R. (2017).Scarf: A social-aware reliable forwarding technique for vehicular communications. In Proceedings of the 3rd Workshop on Experiences with the Design and Implementation of Smart Objects, 1–6.
   Google Scholar
• Velayudhan, N. C., Arulappan, A., & Madanan, M. (2022). Sybil attack with RSU detection and location privacy in Urban VANETs- an efficient EPORP technique. Wireless Personal Communications, 122(4), 3573–3601. https://doi.org/10.1007/s11277-021-09102-x
   Web of Science ®Google Scholar
• Wendi, C. O., Uddin, M., Ansere, J. A., Nkurunziza, P., Anajemba, J. H., & Bashir, A. K. (2018). On detection of Sybil attack in large-scale VANETs using spider-monkey technique. IEEE Access, 6, 47258–47267. https://doi.org/10.1109/ACCESS.2018.2864111
   Web of Science ®Google Scholar
• Xiong, Y., Ma, J., Wang, W., & Tu, D. (2013). Roadgate: Mobility-centric roadside units deployment for vehicular networks. International Journal of Distributed Sensor Networks, 9(3), 690974. https://doi.org/10.1155/2013/690974
   Google Scholar
• Yao, Y., Xiao, B., Yang, G., Hu, Y., Wang, L., & Zhou, X. (2019). Power control identification: A novel Sybil attack detection scheme in vanets using RSSI. IEEE Journal on Selected Areas in Communications, 37(11), 2588–2602. https://doi.org/10.1109/JSAC.2019.2933888
   Web of Science ®Google Scholar
• Zhu, J. (2018). Wireless sensor Network technology based on security trust evaluation model. International Journal of Online Engineering, 14(4), 211–226. https://doi.org/10.3991/ijoe.v14i04.8590
   Google Scholar