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Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm

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Abstract

In vehicular ad hoc networks (VANETs), Sybil attacks are serious security problems that can seriously affect the operations of the VANETs by producing fake identities and routes. To deal with this problem, in this paper, we present a cross-layer approach and fuzzy logic-based solution that should be applied in roadside units (RSUs). This scheme applies two fuzzy logic controllers (FLCs), in which the first one considers factors such as vehicles trust, Received signal strength indication (RSSI) difference, vehicle distance, and vehicle angle while the second one utilizes factors such as signal-to-noise ratio, network entry time, number of the neighbors, and buffer size. In addition, the arithmetic optimization algorithm (AOA) is applied for tuning the applied fuzzy sets and selecting the best possible rules in the proposed FLCs to improve their performance. Extensive simulation results were conducted using the NS2 and SUMO simulators for two scenarios, which the first one is allocated for in-town and the other is considered for out-town environments. The achieved results indicate that the proposed Sybil attack detection scheme outperforms other approaches in terms of the various metrics such as false positive rate (FPR), the number of dropped packets, and packet loss ratio are used.

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References

  1. Hasrouny H et al (2017) VANet security challenges and solutions: A survey. Veh Commun 7:7–20

    Google Scholar 

  2. Mejri MN, Ben-Othman J, Hamdi M (2014) Survey on VANET security challenges and possible cryptographic solutions. Veh Commun. https://doi.org/10.1016/j.vehcom.2014.05.001

    Article  Google Scholar 

  3. Arena F, Pau G (2019) An overview of vehicular communications. Future Internet. https://doi.org/10.3390/fi11020027

    Article  Google Scholar 

  4. Singh GD et al (2018) A review on VANET routing protocols and wireless standards in smart computing and informatics. Springer, London, pp 329–340

    Google Scholar 

  5. Zeeshan A, Naz S, Jamil A (2020) Minimizing transmission delays in vehicular ad hoc networks by optimized placement of road-side unit. Wireless Netw 26(4):2905–2914

    Google Scholar 

  6. Saif Al-Sultan N, Moath M, Al-Doori AH, Al-Bayatti HZ (2014) A comprehensive survey on vehicular ad hoc network. J Netw Comput Appl. https://doi.org/10.1016/j.jnca.2013.02.036

    Article  Google Scholar 

  7. Ganeshkumar N, Sanjay K (2021) Obu (on-board unit) wireless devices in vanet (s) for effective communication—A review. Computational Methods and Data Engineering. https://doi.org/10.1007/978-981-15-7907-3_15

    Article  Google Scholar 

  8. Yang Fet al(2020) Spectral efficiency optimization and interference management for multi-hop D2D communications in VANETs. IEEE Trans Veh Technol 69(6): 6422–6436 https://doi.org/10.1109/TVT.2020.2987526

  9. Sumra IA, Hasbullah J-l HB, AbManan B (2015) Attacks on security goals confidentiality, integrity, availability in VANET: a survey in vehicular ad-hoc networks for smart cities springer. London 51:61

    Google Scholar 

  10. Engoulou RG et al (2014) VANET security surveys. Comput Commun 44:1–13

    Google Scholar 

  11. Masdari M, Jalali M (2016) A survey and taxonomy of DoS attacks in cloud computing. Sec Commun Net 9(16):3724–3751

    Google Scholar 

  12. Qu F et al (2015) A security and privacy review of VANETs. IEEE Trans Intell Transp Syst 16(6):2985–2996

    Google Scholar 

  13. Sharma S, Kaul A (2018) A survey on intrusion detection systems and honeypot based proactive security mechanisms in VANETs and VANET Cloud. Veh commun 12:138–164

    Google Scholar 

  14. Grover J, Gaur M, Laxmi V (2016) Sybil attack in VANETs: detection and prevention in security of self-organizing networks. Auerbach Publications, United States, pp 287–312

    Google Scholar 

  15. Kaur S, A Kumar (2016) Techniques to isolate sybil attack in VANET-A review. in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). 3–5 March 2016.IEEE

  16. Bhise AM, Kamble SD (2016) Review on detection and mitigation of Sybil attack in the network. Procedia Comput Sci 78:395–401

    Google Scholar 

  17. Masdari M, Khezri H (2020) A survey and taxonomy of the fuzzy signature-based intrusion detection systems. Appl Soft Comput 92:106301

    Google Scholar 

  18. Lee SW et al (2021) Towards secure intrusion detection systems using deep learning techniques: comprehensive analysis and review. J Netw Comput Appl. https://doi.org/10.1016/j.jnca.2021.103111

    Article  Google Scholar 

  19. Jafarian T et al (2021) A survey and classification of the security anomaly detection mechanisms in software defined networks. Clust Comput 24(2):1235–1253

    Google Scholar 

  20. Masdari M et al (2011) (2011)A survey and taxonomy of distributed certificate authorities in mobile ad hoc networks. EURASIP J Wirel Commun Net 1:1–12

    Google Scholar 

  21. Masdari M (2017) Markov chain-based evaluation of the certificate status validations in hybrid MANETs. J Netw Comput Appl 80:79–89

    Google Scholar 

  22. Chaubey N K, Yadav D (2020) A taxonomy of Sybil attacks in vehicular ad-hoc network (VANET) in IoT and cloud computing advancements in vehicular Ad-Hoc Networks., IGI Global. pp 174–190

  23. Vasudeva A, Sood M (2018) Survey on sybil attack defense mechanisms in wireless ad hoc networks. J Netw Comput Appl 120:78–118

    Google Scholar 

  24. Abdulkader ZA et al (2018) A survey on sybil attack detection in vehicular ad hoc networks (VANET). J Comput 29(2):1–6

    Google Scholar 

  25. Masdari M, Ahmadzadeh S, Bidaki M (2017) Key management in wireless body area network: challenges and issues. J Netw Comput Appl 91:36–51

    Google Scholar 

  26. Masdari M, Ahmadzadeh S (2016) Comprehensive analysis of the authentication methods in wireless body area networks. Sec commun Net 9(17):4777–4803

    Google Scholar 

  27. Mishra AK et al (2018) Analytical model for sybil attack phases in internet of things. IEEE Internet Things J 6(1):379–387

    Google Scholar 

  28. Abualigah L et al (2021) The arithmetic optimization algorithm. Comput Method Appl Mech Eng 376:113609

    MathSciNet  MATH  Google Scholar 

  29. Ayaida M et al (2019) A macroscopic traffic model-based approach for sybil attack detection in VANETs. Ad Hoc Netw 90:101845

    Google Scholar 

  30. Iwendi C et al (2018) On detection of Sybil attack in large-scale VANETs using spider-monkey technique. IEEE Access 6:47258–47267

    Google Scholar 

  31. Yao Y et al (2018) Multi-channel based Sybil attack detection in vehicular ad hoc networks using RSSI. IEEE Trans Mob Comput 18(2):362–375

    Google Scholar 

  32. Velayudhan NC, Anitha A, Madanan M (2021) Sybil attack detection and secure data transmission in VANET using CMEHA-DNN and MD5-ECC. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-03379-3

    Article  Google Scholar 

  33. Velayudhan NC, Anitha A, Madanan M (2021) Sybil Attack with RSU detection and location privacy in urban VANETs: an efficient EPORP technique. Wire Pers Commun 122(4):1–29

    Google Scholar 

  34. Sefati SS, Tabrizi SG (2021) Detecting sybil attack in vehicular Ad-hoc networks (Vanets) by using fitness function signal strength index and throughput. Wire Pers Commun 123(3):1–21

    Google Scholar 

  35. Feng X et al (2017) A method for defensing against multi-source Sybil attacks in VANET. Peer-to-Peer Net Appl 10(2):305–314

    Google Scholar 

  36. Hamdan S, Hudaib A, Awajan A (2021) Detecting Sybil attacks in vehicular ad hoc networks. Int J Parallel Emergent Distrib Syst 36(2):69–79

    Google Scholar 

  37. Parham M, Pouyan AA (2020) An effective privacy-aware Sybil attack detection scheme for secure communication in vehicular ad hoc network. Wireless Pers Commun 113(2):1149–1182

    Google Scholar 

  38. Putra G D, Sulistyo S (2017) Trust based approach in adjacent vehicles to mitigate sybil attacks in vanet. In Proceedings of the International Conference on Software and e-Business. 2017

  39. Faisal SM, Zaidi T (2020) Timestamp based detection of sybil attack in VANET. Int J Netw Secur 22(3):397–408

    Google Scholar 

  40. Park S et al (2013) Defense against Sybil attack in the initial deployment stage of vehicular ad hoc network based on roadside unit support. Sec Commun Net 6(4):523–538

    Google Scholar 

  41. Feng X, Tang J (2017) Obfuscated RSUs vector based signature scheme for detecting conspiracy Sybil attack in VANETs. Mob Inf Syst 22:1–11

    Google Scholar 

  42. Rajadurai H, Gandhi UD (2020) Fuzzy based collaborative verification system for Sybil attack detection in MANET. Wireless Pers Commun 110(4):2179–2193

    Google Scholar 

  43. Jan MA et al (2018) A Sybil attack detection scheme for a forest wildfire monitoring application. Futur Gener Comput Syst 80:613–626

    Google Scholar 

  44. Wang Cet al (2018) Accurate sybil attack detection based on fine-grained physical channel information. Sensors 18(3): 878. https://doi.org/10.3390/s18030878

  45. Yao Y et al (2019) Power control identification: a novel Sybil attack detection scheme in VANETs using RSSI. IEEE J Sel Areas Commun 37(11):2588–2602

    Google Scholar 

  46. Hosseinzadeh M et al (2021) Improving security using SVM-based anomaly detection: issues and challenges. Soft Comput 25(4):3195–3223

    Google Scholar 

  47. Newsome J et al (2004) The sybil attack in sensor networks: analysis & defenses. in Third international symposium on information processing in sensor networks. IPSN 2004. IEEE

  48. Mirjalili S (2019) Genetic algorithm in evolutionary algorithms and neural networks. Springer, US, pp 43–55

    Google Scholar 

  49. Costa D (1994) tabu search algorithm for computing an operational timetable. Eur J Oper Res 76(1):98–110

    MATH  Google Scholar 

  50. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing: theory and applications. Springer, USA, pp 7–15

    MATH  Google Scholar 

  51. Kennedy J, Eberhart R (1995) Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks. 27 Nov.-1 Dec. 1995. IEEE

  52. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39

    Google Scholar 

  53. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput. https://doi.org/10.1108/02644401211235834

    Article  Google Scholar 

  54. Karaboga D et al (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Google Scholar 

  55. Masdari M, Barshande S, Ozdemir S (2019) CDABC: chaotic discrete artificial bee colony algorithm for multi-level clustering in large-scale WSNs. J Supercomput 75(11):7174–7208

    Google Scholar 

  56. Calheiros RN et al (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract exp. https://doi.org/10.1002/spe.995

    Article  Google Scholar 

  57. Jeong H-J, Lee H -J,Shin C H,Moon S -M (2018) IONN: Incremental offloading of neural network computations from mobile devices to edge servers. In Proceedings of the ACM Symposium on Cloud Computing. https://doi.org/10.1145/3267809.3267828

  58. Rabieh K et al(2015) Cross-layer scheme for detecting large-scale colluding Sybil attack in VANETs. in 2015 IEEE International Conference on Communications (ICC). IEEE. DOI:https://doi.org/10.1109/ICC.2015.7249492

  59. Tyagi P, Dembla D (2017) Performance analysis and implementation of proposed mechanism for detection and prevention of security attacks in routing protocols of vehicular ad-hoc network (VANET). Egypt Informat J. https://doi.org/10.1016/j.eij.2016.11.003

    Article  Google Scholar 

  60. Monica D (2009) Thwarting the sybil attack in wireless ad hoc networks. Inst Super Tec, Punjab

    Google Scholar 

  61. Masdari M (2017) Towards secure localized certificate revocation in mobile ad-hoc networks. IETE Tech Rev. https://doi.org/10.1080/02564602.2016.1215270

    Article  Google Scholar 

  62. Masdari M, Bidaki M, Naghiloo F (2017) Comprehensive evaluation of the localized certificate revocation in mobile ad hoc network. Wireless Pers Commun 94(3):977–1001

    Google Scholar 

  63. Karimi M, Sadeghi R (2021) Improvement of Sybil attack detection in vehicular ad-Hoc networks using cross-layer and fuzzy Logic. Majlesi J Elect Eng 15(1):9. https://doi.org/10.52547/mjee.15.1.9

    Article  Google Scholar 

  64. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  65. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

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Correspondence to Mohammad Ali Tabarzad.

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Maleknasab Ardakani, M., Tabarzad, M.A. & Shayegan, M.A. Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm. J Supercomput 78, 16303–16335 (2022). https://doi.org/10.1007/s11227-022-04526-z

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