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Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs

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Abstract

Vehicular ad hoc networks (VANETs) have the ability to make changes in travelling and driving mode of people and so on, in which vehicle can broadcast and forward the message related to emergency or present road condition. The safety and efficiency of modern transportation system is highly improved using VANETs. However, the vehicular communication performance is weakened with the sudden emergence of distributed denial of service (DDoS) attacks. Among other attacks, DDoS attack is the fastest attack degrading the VANETs performance due to its node mobility nature. Also, the attackers (cyber terrorists, politicians, etc.) have now considered the DDoS attack as a network service degradation weapon. In current trend, there is a quick need for mitigation and prevention of DDoS attacks in the exploration field. To resolve the conflict of privacy preservation, we propose a fast and secure HCPDS based framework for DDoS attack detection and prevention in VANETs. The Road Side Units (RSUs) have used HCPDS algorithm to evaluate the fitness values of all vehicles. This evaluation process is done for effective detection of spoofing and misbehaving nodes by comparing the obtained fitness value with the statistical information (packet factors, RSU zone, and vehicle dynamics) gathered from the vehicles. The credentials of all worst nodes are cancelled to avoid further communication with other vehicles. In HCPDS algorithm, the PSO updation strategy is added to Dragon fly algorithm to improve the search space. In addition, Chaos theory is applied to tune the parameters of proposed HCPDS algorithm. From the experimental results, it proved that the HCPDS based proposed approach can efficiently meet the requirements of security and privacy in VANETs.

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References

  1. Wafa, B. J., Mauro, C., & Chhagan, L. (2020). Security and design requirements for software-defined VANETs. Computer Networks, 169, 107099.

    Google Scholar 

  2. Deb, N., Chakraborty, M., & Chaki, N. (2014). CORIDS: A cluster-oriented reward-based intrusion detection system for wireless mesh networks. Security and Communication Networks, 7(3), 532–543.

    Google Scholar 

  3. Kumar, N., & Chilamkurti, N. (2014). Collaborative trust aware intelligent intrusion detection in VANETs. Computers & Electrical Engineering, 40(6), 1981–1996.

    Google Scholar 

  4. Misra, S., Venkata Krishna, P., & Abraham, K. I. (2011). A stochastic learning automata based solution for intrusion detection in vehicular ad hoc networks. Security and Communication Networks, 4(6), 666–677.

    Google Scholar 

  5. Jiang, S., Zhu, X., & Wang, L. (2016). An efficient anonymous batch authentication scheme based on HMAC for VANETs. IEEE Transactions on Intelligent Transportation Systems, 17(8), 2193–2204.

    Google Scholar 

  6. Li, Q., Malip, A., Martin, K. M., Ng, S.-L., & Zhang, J. (2012). A reputation-based announcement scheme for VANETs. IEEE Transactions on Vehicular Technology, 61(9), 4095–4108.

    Google Scholar 

  7. Kerrache, C. A., Lagraa, N., Calafate, C. T., & Lakas, A. (2017). TFDD: A trust-based framework for reliable data delivery and DoS defense in VANETs. Vehicular Communications, 9, 254–267.

    Google Scholar 

  8. Vinu, S. (2019). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications, 104(1), 173–197.

    Google Scholar 

  9. Li, Z., & Chigan, C. T. (2014). On joint privacy and reputation assurance for vehicular ad hoc networks. IEEE Transactions on Mobile Computing, 13(10), 2334–2344.

    Google Scholar 

  10. Mahmoud, M. E., & Shen, X. (2011). An integrated stimulation and punishment mechanism for thwarting packet dropping attack in multihop wireless networks. IEEE Transactions on Vehicular Technology, 60(8), 3947–3962.

    Google Scholar 

  11. Prabakeran Saravanan, Sethukarasi, T., & Indumathi, V. (2018). Authentic novel trust propagation model with deceptive recommendation penalty scheme for distributed denial of service attacks. Journal of Computational and Theoretical Nanoscience, 15(6), 2383–2389.

    Google Scholar 

  12. Prabakeran Saravanan, Sethukarasi, T., & Indumathi, V. (2018). An efficient software defined network based cooperative scheme for mitigation of distributed denial of service (DDoS) attacks. Journal of Computational and Theoretical Nanoscience, 15(6), 2221–2226.

    Google Scholar 

  13. Saravanan, P., Sethukarasi, T., & Indumathi, V. (2018). Two level trust propagation model with global weighted index for detecting and mitigating denial of service. TAGA Journal of Graphic Technology, 14, 1748–0345.

    Google Scholar 

  14. Saravanan, P., Swarnapriya, K., & Agnes Priscilla Remina. (2019). Multi-defense framework for mitigating man in the cloud attack (MitC). i-Manager's Journal on Computer Science, 7(2), 8.

    Google Scholar 

  15. Vinu Sundararaj. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. International Journal of Intelligent Engineering and Systems, 9(3), 117–126.

    Google Scholar 

  16. Vinu, S., Selvi, M., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers and Security, 77, 277–288.

    Google Scholar 

  17. Hussain, R., Hussain, F., & Zeadally, S. (2019). Integration of VANET and 5G security: A review of design and implementation issues. Future Generation Computer Systems, 101, 843–864.

    Google Scholar 

  18. Petit, J., Schaub, F., Feiri, M., & Kargl, F. (2014). Pseudonym schemes in vehicular networks: A survey. IEEE Communications Surveys & Tutorials, 17(1), 228–255.

    Google Scholar 

  19. Buttyán, L., Tamás H., André W., & William W. (2009). Slow: A practical pseudonym changing scheme for location privacy in vanets. In 2009 IEEE vehicular networking conference (VNC) (pp. 1–8). IEEE.

  20. Emara, K. (2017). Safety-aware location privacy in VANET: Evaluation and comparison. IEEE Transactions on Vehicular Technology, 66(12), 10718–10731.

    Google Scholar 

  21. Freudiger, J., Mohammad H. M., Jean-Yves L. B., & Jean-Pierre H. (2010). On the age of pseudonyms in mobile ad hoc networks. In 2010 Proceedings IEEE INFOCOM (pp. 1–9). IEEE.

  22. Lu, R., Lin, X., Luan, T. H., Liang, X., & Shen, X. (2011). Pseudonym changing at social spots: An effective strategy for location privacy in vanets. IEEE Transactions on Vehicular Technology, 61(1), 86–96.

    Google Scholar 

  23. Arif, M., Guojun Wang, M., Bhuiyan, Z. A., Wang, T., & Chen, J. (2019). A survey on security attacks in VANETs: Communication, applications and challenges. Vehicular Communications, 19, 100179.

    Google Scholar 

  24. Pan, Y., & Li, J. (2013). Cooperative pseudonym change scheme based on the number of neighbors in VANETs. Journal of Network and Computer Applications, 36(6), 1599–1609.

    MathSciNet  Google Scholar 

  25. Sun, Y., Zhang, B., Zhao, B., Xiangyu, S., & Jinshu, S. (2015). Mix-zones optimal deployment for protecting location privacy in VANET. Peer-to-Peer Networking and Applications, 8(6), 1108–1121.

    Google Scholar 

  26. Ullah, I., Abdul W., Munam A. S., & Abdul W. (2017). VBPC: Velocity based pseudonym changing strategy to protect location privacy of vehicles in VANET. In 2017 International conference on communication technologies (ComTech) (pp. 132–137). IEEE.

  27. Wasef, A., & Shen, X. S. (2010). REP: Location privacy for VANETs using random encryption periods. Mobile Networks and Applications, 15(1), 172–185.

    Google Scholar 

  28. Ying, B., Makrakis, D., & Hou, Z. (2015). Motivation for protecting selfish vehicles’ location privacy in vehicular networks. IEEE Transactions on Vehicular Technology, 64(12), 5631–5641.

    Google Scholar 

  29. Bayat, M., Barmshoory, M., Rahimi, M., & Aref, M. R. (2015). A secure authentication scheme for VANETs with batch verification. Wireless Networks, 21(5), 1733–1743.

    Google Scholar 

  30. He, D., Zeadally, S., Baowen, X., & Huang, X. (2015). An efficient identity-based conditional privacy-preserving authentication scheme for vehicular ad hoc networks. IEEE Transactions on Information Forensics and Security, 10(12), 2681–2691.

    Google Scholar 

  31. Huang, D., Misra, S., Verma, M., & Xue, G. (2011). PACP: An efficient pseudonymous authentication-based conditional privacy protocol for VANETs. IEEE Transactions on Intelligent Transportation Systems, 12(3), 736–746.

    Google Scholar 

  32. Kumaresan, G., & Adiline Macriga, T. (2017). Group key authentication scheme for VANET intrusion detection (GKAVIN). Wireless Networks, 23(3), 935–945.

    Google Scholar 

  33. Lee, C.-C., & Lai, Y.-M. (2013). Toward a secure batch verification with group testing for VANET. Wireless Networks, 19(6), 1441–1449.

    Google Scholar 

  34. Li, W., & Song, H. (2015). ART: An attack-resistant trust management scheme for securing vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 17(4), 960–969.

    Google Scholar 

  35. Zhao, H., Xin Y., & Xiaolin L. (2010). cTrust: trust aggregation in cyclic mobile ad hoc networks. In European conference on parallel processing (pp. 454–465). Springer, Berlin.

  36. Jesudoss, A., Kasmir Raja, S. V., & Sulaiman, A. (2015). Stimulating truth-telling and cooperation among nodes in VANETs through payment and punishment scheme. Ad Hoc Networks, 24, 250–263.

    Google Scholar 

  37. Wahab, O. A., Otrok, H., & Mourad, A. (2014). A cooperative watchdog model based on Dempster-Shafer for detecting misbehaving vehicles. Computer Communications, 41, 43–54.

    Google Scholar 

  38. Minhas, U. F., Zhang, J., Tran, T., & Cohen, R. (2010). A multifaceted approach to modeling agent trust for effective communication in the application of mobile ad hoc vehicular networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(3), 407–420.

    Google Scholar 

  39. Butun, I. (2017). Privacy and trust relations in internet of things from the user point of view. In 2017 IEEE 7th annual computing and communication workshop and conference (CCWC) (pp. 1–5). IEEE.

  40. Chen, Y.-M., & Wei, Y.-C. (2013). A beacon-based trust management system for enhancing user centric location privacy in VANETs. Journal of Communications and Networks, 15(2), 153–163.

    Google Scholar 

  41. Wang, J., Zhang, Y., Wang, Y., & Xiang, G. (2016). RPRep: A robust and privacy-preserving reputation management scheme for pseudonym-enabled VANETs. International Journal of Distributed Sensor Networks, 12(3), 6138251.

    Google Scholar 

  42. Sun, Y., Rongxing, L., Lin, X., Shen, X., & Jinshu, S. (2010). An efficient pseudonymous authentication scheme with strong privacy preservation for vehicular communications. IEEE Transactions on Vehicular Technology, 59(7), 3589–3603.

    Google Scholar 

  43. Zhao, H., Xin Y., & Xiaolin L. (2010). cTrust: Trust aggregation in cyclic mobile ad hoc networks. In European conference on parallel processing (pp. 454–465). Springer, Berlin.

  44. Malhi, A. K., & Batra, S. (2016). Genetic-based framework for prevention of masquerade and DDoS attacks in vehicular ad-hoc networks. Security and Communication Networks, 9(15), 2612–2626.

    Google Scholar 

  45. Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on Micro machine and human science, 1995. MHS’95 (pp. 39–43).

  46. Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Berlin: Springer.

    Google Scholar 

  47. Ludwig, A., & Schmidt, K. D. (2010). Gauss–Markov loss prediction in a linear model. Casualty Actuarial Society E-Forum, Fall, 2010, 1–48.

    Google Scholar 

  48. Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.

    MathSciNet  Google Scholar 

  49. Alatas, B., Akin, E., & Bedri Ozer, A. (2009). Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals, 40(4), 1715–1734.

    MathSciNet  MATH  Google Scholar 

  50. Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, 18(1), 89–98.

    MathSciNet  MATH  Google Scholar 

  51. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Google Scholar 

  52. G´omez M´armol, F., & Mart´ınez P´erez, G. (2012). Trip, A trust and reputation infrastructure-based proposal for vehicular ad hoc networks. Journal of Network and Computer Applications, 35(3), 934–941.

    Google Scholar 

  53. Sedjelmaci, H., & Senouci, S. M. (2015). An accurate and efficient collaborative intrusion detection framework to secure vehicular networks. Computers & Electrical Engineering, 43, 33–47.

    Google Scholar 

  54. Sundararaj, V. (2019). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325.

    Google Scholar 

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Authors and Affiliations

  1. Department of CSE, KCG College of Technology, Karapakkam, Tamilnadu, India

    S. Prabakeran

  2. Department of CSE, RMK Engineering College, Chennai, Tamilnadu, India

    T. Sethukarasi

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  1. S. Prabakeran
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Correspondence to S. Prabakeran.

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Prabakeran, S., Sethukarasi, T. Optimal solution for malicious node detection and prevention using hybrid chaotic particle dragonfly swarm algorithm in VANETs. Wireless Netw 26, 5897–5917 (2020). https://doi.org/10.1007/s11276-020-02413-0

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