International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Analysis of Machine Learning Classifiers to Detect Malicious Node in Vehicular Cloud Computing

Author NameAuthor Details

A. Sheela Rini, C. Meena

A. Sheela Rini[1]

C. Meena[2]

[1]Department of Computer Science, Avinashilingam Institute for Home Science & Higher Education for Women, Coimbatore, Tamil Nadu, India

[2]Department of Computer Science, Avinashilingam Institute for Home Science & Higher Education for Women, Coimbatore, Tamil Nadu, India

Abstract

VANET or Vehicular networks are created using the principles of MANETS and are used by intelligent transport systems to offer efficient communication between the domains of vehicles. Increasing the number of vehicles requires communication between vehicles to be fast and secure, where cloud computing with VANET is more prominent. To provide a secure VANET communication environment, this paper proposes a malicious or hacked vehicle identification system. Malicious vehicles are identified using four steps. The first step uses a clustering algorithm for similar group vehicles. In the Second step, cluster heads are identified and elected. In the next step, Multiple Point Relays are selected. Finally, classifiers are used to identify hacked vehicles. However, the existing system performance degrades as soon as the number of vehicles increases, resulting in increased cost during Cluster head election, inability to produce stable clusters, and the need for accurate and fast classification in high traffic scenarios. This work improves clustering algorithms and examines several classification algorithms to solve these issues. The classifiers analyzed are Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Naïve-Bayes (NB). A Hybrid classifier that combines SVM and KNN classifiers is also analyzed for its effectiveness to detect malicious vehicles. From the experimental results, it could be observed that the detection accuracy is high while using the hybrid classifier.

Index Terms

VANET

Malicious Node

SVM

Decision Tree

Naïve-Bayes

KNN

Reference

  1. 1.
    Ramakrishnan, B., Rajesh, R. S., & Shaji, R. S. (2010). Performance analysis of 802.11 and 802.11 p in cluster based simple highway model. International Journal of Computer Science and Information Technologies, 1(5), 420-426.
  2. 2.
    E. S. A. Ahmed and R. A. Saeed, “A survey of big data cloud computing security, “International Journal of Computer Science and Software Engineering (IJCSSE), vol. 3, no. 1, pp. 78–85, 2014.
  3. 3.
    Katuka, Jatau Isaac, and Muhammad Shafie Abd Latiff. “Vanets and Its Related Issues: An Extensive Survey.” Journal of Theoretical & Applied Information Technology 66.1, 2014.
  4. 4.
    H. Wu, “Developing vehicular data cloud services in the IoT environment, “IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1587–1595, 2014.
  5. 5.
    A. Eltahir, R. A. Saeed, A. Mukherjee, and M. K. Hasan, “Evaluation and analysis of an enhanced hybrid wireless mesh protocol for vehicular ad-hoc network,” EURASIP Journal on Wireless Communications and Networking, vol. 1, pp. 1–11, 2016.
  6. 6.
    M. K. Hasan, A. F. Ismail, A.-H. Abdalla, H. A. M. Ramli, W. Hashim, and S. Islam, “Throughput maximization for the cross-tier interference in heterogeneous network,” Advanced Science Letters, vol. 22, no. 10, pp. 2785–2789, 2016.
  7. 7.
    Wahab OA, Otrok H and Mourad A. “VANET QoSOLSR: QoS-based clustering protocol for vehicular ad hoc networks”, Computer Communication; 36: 1422–1435, 2013.
  8. 8.
    Joe, M. Milton & Ramakrishnan, Balasundaram. (2016). Review of vehicular ad hoc network communication models including WVANET (Web VANET) model and WVANET future research directions. Wireless Networks. 22. 10.1007/s11276-015-1104-z.
  9. 9.
    Y. Mao, “A survey on mobile edge computing: the communication perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017.
  10. 10.
    Z. K. A. Mohammed and E. S. A. Ahmed, “Internet of things applications, challenges and related future technologies,” WSN, vol. 67, no. 2, pp. 126–148, 2017.
  11. 11.
    J. Xu, “Joint service caching and task offloading for mobile edge computing in dense networks,” in Proceedings of the IEEE Conference on Computer Communications, Honolulu, HI, USA, 2018.
  12. 12.
    Nobahary, Sanaz, and ShahramBabaie. “A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network.” Appl. Comput. Syst. 23.2: 118-127, 2018.
  13. 13.
    Shams EA, Rizaner A, Ulusoy AH (2018), “Trust aware support vector machine intrusion detection and prevention system in vehicular ad hoc networks” Computers & Security 78:245– 254.
  14. 14.
    A. H. Sodhro, Z. Luo, G. H. Sodhro, M. Muzamal, J. J. P. C. Rodrigues, and V. H. C. de Albuquerque, “Artificial Intelligence based QoS optimization for multimedia communication in IoV systems,” Future Generation Computer Systems, vol. 95, pp. 667–680, 2019.
  15. 15.
    H. Yang, A. Alphones, Z. Xiong, D. Niyato, J. Zhao, and K. Wu, “Artificial intelligence-enabled intelligent 6G networks,” 2019, https://arxiv.org/abs/1912.05744.
  16. 16.
    W. Tong, A. Hussain, W. X. Bo, and S. Maharjan, “Artificial intelligence for vehicle-to-everything: a survey,” IEEE Access, vol. 7, pp. 10823–10843, 2019.
  17. 17.
    Y. Dai, D. Xu, S. Maharjan, G. Qiao, and Y. Zhang, “Artificial intelligence empowered edge computing and caching for internet of vehicles,” IEEE Wireless Communications, vol. 26, no. 3, pp. 12–18, 2019.
  18. 18.
    Nascimento, Douglas & Iano, Yuzo & Loschi, Hermes Jose & Razmjooy, Navid & Sroufe, Robert & Oliveira, Vlademir & Pajuelo Castro, Diego Arturo & Montagner, Matheus,. Sustainable Adoption of Connected Vehicles in the Brazilian Landscape: Policies, Technical Specifications and Challenges. Transactions on Environment and Electrical Engineering. 3. 44-62, 2019.
  19. 19.
    H. Ji, “Artificial intelligence-empowered edge of vehicles: architecture, enabling technologies, and applications,” IEEE Acces, vol. 8, pp. 61020–61034, 2020.
  20. 20.
    M. B. Hassan, E. S. Ali, R. A. Mokhtar, R. A. Saeed, and B. S. Chaudhari, “NB-IoT: concepts, applications, and deployment challenges, book chapter (ch 6),” in LPWAN Technologies for IoT and M2MApplications, B. S. Chaudhari and M. Zennaro, Eds., Elsevier, Berlin, Germany, 2020.
  21. 21.
    Z. E. Ahmed, M. K. Hasan, R. A. Saeed et al., “Optimizing energy consumption for cloud internet of things,” Frontiers of Physics, vol. 8, p. 358, 2020.
  22. 22.
    Bibi, Rozi, et al. “Edge AI-based automated detection and classification of road anomalies in VANET using deep learning.” Computational intelligence and neuroscience, 2021.
  23. 23.
    Mohammadnia, A., Alguliyev, R., Yusifov, F., &Jamali, S., “Routing algorithm for vehicular Ad Hoc network based on dynamic Ant Colony optimization”, International Journal of Electronics and Electrical Engineering, 4, 79–83, 2016.
  24. 24.
    L. Song, G. Sun, H. Yu, X. Du, and M. Guizani, “FBIA: a fog-based identity authentication scheme for privacy preservation in internet of vehicles,” IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5403–5415, 2020.
  25. 25.
    Z. Meng, “Security enhanced internet of vehicles with Cloud?-Fog?Dew computing,” ZTE Communications, vol. 15, no. S2, 2017
  26. 26.
    J. Ramkumar and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) For Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2020, doi: http://dx.doi.org/10.12785/ijcds/100196.
  27. 27.
    J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
  28. 28.
    J. Ramkumar and R. Vadivel, “Improved frog leap inspired protocol (IFLIP) – for routing in cognitive radio ad hoc networks (CRAHN),” World J. Eng., vol. 15, no. 2, pp. 306–311, 2018, doi: 10.1108/WJE-08-2017-0260.
  29. 29.
    J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., pp. 1–23, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
  30. 30.
    J. Ramkumar and R. Vadivel, “Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 8, pp. 4549–4554, 2020, doi: 10.30534/ijeter/2020/82882020.
  31. 31.
    M. Lingaraj, T. N. Sugumar, C. Stanly Felix, and J. Ramkumar, “Query Aware Routing Protocol for Mobility Enabled Wireless Sensor Network,” Int. J. Comput. Networks Appl., vol. 8, no. 3, p. 258, Jun. 2021, doi: 10.22247/IJCNA/2021/209192.
SCOPUS
SCImago Journal & Country Rank