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Hybrid Algorithm to Detect DDoS Attacks in VANETs

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Abstract

Security and safety are fundamental issues in any wireless network. The problem becomes serious when the specified network is Vehicular Adhoc Network (VANET). VANET faces Distributed Denial of Service (DDoS) attacks, when several vehicles carry out various types of Denial of Service (DoS) attacks to disrupt the normal functioning of network, thereby endangering human lives. A highly efficient and reliable algorithm is required to be developed to detect and prevent DDoS attacks in VANET. This paper presents a hybrid detection algorithm based on the SVM kernel methods of AnovaDot and RBFDot for detecting DDoS attacks in VANETs. In this hybrid algorithm, features like collisions, packet drop, jitter etc. have been used to simulate real time network communication scenario where the network is operating under normal conditions, as well as under DDoS attacks. These features are used both for training and for testing the model based on the proposed hybrid algorithm. The performance of the model based on the proposed hybrid algorithm is compared with the models based on single SVM kernel algorithms AnovaDot and RBFDot based on Accuracy, Gini, KS, MER and H. The experimental results show that the model based on the proposed hybrid algorithm is superior to detect DDoS attacks as compared to the models based on single SVM kernel algorithms AnovaDot and RBFDot. The results also prove that by combining the the SVM kernel algorithms, an efficient and effective hybrid algorithm can be developed.

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References

  1. Liang, W., Li, Z., Zhang, H., Wang, S., & Bie, R. (2015). Vehicular ad hoc networks: Architectures, research issues, methodologies, challenges, and trends. International Journal of Distributed Sensor Networks, 2015, 745303.

    Google Scholar 

  2. Ayyappan, B., & Mohan Kumar, P. (2016). Vehicular ad hoc networks (VANET): Architectures, methodologies and design issues. 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM)., 2016, 177–180.

    Google Scholar 

  3. Bariah, L., Shehada, D., Salahat, E., Yeun, C.Y. (2016). Recent advances in VANET security: A survey. 2015 IEEE 82nd Veh. Technol. Conf. VTC Fall 2015—Proc.

  4. Al-Sultan, S., Al-Doori, M. M., Al-Bayatti, A. H., & Zedan, H. (2014). A comprehensive survey on vehicular ad hoc network. Journal of Network and Computer Applications, 37(1), 380–392.

    Google Scholar 

  5. Ho, Y. H., & Hua, K. A. (2010). Failure-resilient vehicular networks. In 35th Annual IEEE Conference on Local Computer Networks, LCN, 25(8), 336–339.

    Google Scholar 

  6. Saha, S., Roy, U., & Sinha, D. D. (2013). VANET simulation in diffrent indian city scenario. Advance in Electronic and Electric Engineering, ISSN, 3(9), 1221–1228.

    Google Scholar 

  7. Kait R., & Chauhan, R. K. (2011) Networks on road—challenges in securing vehicular Adhoc networks. An International Journal of Engineering Sciences, 1, 53–61.

    Google Scholar 

  8. Kim, S. (2018). Blockchain for a trust network among intelligent vehicles. Advances in Computers, 111, 43–68.

    Google Scholar 

  9. Bouk, S. H., Ahmed, S. H., & Kim, D. (2015). Vehicular content centric network (VCCN): a survey and research challenges. Proceedings of the 30th Annual ACM Symposium on Applied Computing, 13–17, 695–700.

    Google Scholar 

  10. Kelarestaghi, K.B, Foruhandeh, M., Heaslip, K., Gerdes R. (2019). Survey on vehicular ad hoc networks and its access technologies security vulnerabilities and countermeasures (pp. 1–21). Virginia Tech University.

  11. Kumar, S., & Dutta, K. (2018). Trust based intrusion detection technique to detect selfish nodes in mobile ad hoc networks. Wireless Personal Communications, 101(4), 2029–2052.

    Google Scholar 

  12. Islabudeen, M., & Kavitha Devi, M. K. (2020). A smart approach for intrusion detection and prevention system in mobile ad hoc networks against security attacks. Wireless Personal Communications, 112(1), 193–224.

    Google Scholar 

  13. Ghazy, R. A., El-Rabaie, E. S. M., Dessouky, M. I., El-Fishawy, N. A., & El-Samie, F. E. A. (2020). Feature selection ranking and subset-based techniques with different classifiers for intrusion detection. Wireless Personal Communications, 111(1), 375–393.

    Google Scholar 

  14. Ahmed, H. I., Elfeshawy, N. A., Elzoghdy, S. F., El-sayed, H. S., & Faragallah, O. S. (2017). A neural network-based learning algorithm for intrusion detection systems. Wireless Personal Communications, 97(2), 3097–3112.

    Google Scholar 

  15. Verma, K., Hasbullah, H., & Kumar, A. (2013). Prevention of DoS attacks in VANET. Wireless Personal Communications., 73(1), 95–126.

    Google Scholar 

  16. Kaur, P., Kaur, D., & Mahajan, R. (2017). Wormhole attack detection technique in mobile ad hoc networks. Wireless Personal Communications, 97(2), 2939–2950.

    Google Scholar 

  17. Rampaul, D., Kumar Patial, R., & Kumar, D. (2016). Detection of DoS attack in VANETs. Indian Journal of Science and Technology, 9(47), 1–6.

    Google Scholar 

  18. Shabbir, M., Khan, M. A., Khan, U. S., & Saqib, N. A. (2017). Detection and prevention of distributed denial of service attacks in VANETs. In 2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016, 970–974.

    Google Scholar 

  19. Khalimonenko O.K.A., Badovskaya, E. DDoS attacks in Q1 2018. Kaspersky

  20. Zeadally, S., Hunt, R., Chen, Y. S., Irwin, A., & Hassan, A. (2012). Vehicular ad hoc networks (VANETS): Status, results, and challenges. Telecommunication Systems, 50(4), 217–241.

    Google Scholar 

  21. Raya, M., Papadimitratos, P., Aad, I., Jungels, D., & Hubaux, J. P. (2007). Eviction of misbehaving and faulty nodes in vehicular networks. IEEE Journal on Selected Areas in Communications, 25(8), 1557–1568.

    Google Scholar 

  22. Pathre, A., Agrawal, C., & Jain, A. (2013). Identification of malicious vehicle in vanet environment from Ddos attack. Journal of Global Research in Computer Science, 4(6), 1–5.

    Google Scholar 

  23. Gandhi, U.D., Keerthana, R.V.S.M. (2014) Request response detection algorithm for detecting DoS attack in VANET. In 2014 International Conference on Reliability Optimization and Information Technology (ICROIT) 192–194.

  24. Zhou, T., Choudhury, R. R., Ning, P., & Chakrabarty, K. (2011). P2DAP & #x2014; sybil attacks detection in vehicular ad hoc networks. IEEE Journal on Selected Areas in Communications, 29(3), 582–594.

    Google Scholar 

  25. Adhikary, K., & Bhushan, S. (2017). “Recent techniques used for preventing DOS attacks in VANETs. Proceeding—IEEE International Conference on Computing, Communication and Automation ICCCA, 2017, 564–569.

    Google Scholar 

  26. Singh, P. K., Nandi, S. K., & Nandi, S. (2019). A tutorial survey on vehicular communication state of the art, and future research directions. Vehicular Communications, 18, 100164.

    Google Scholar 

  27. Sharma, S., & Kaushik, B. (2019). A survey on internet of vehicles: Applications, security issues and solutions. Vehicular Communications, 20, 100182.

    Google Scholar 

  28. Loukas, G., Karapistoli, E., Panaousis, E., Sarigiannidis, P., Bezemskij, A., & Vuong, T. (2019). A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles. Ad Hoc Networks, 84, 124–147.

    Google Scholar 

  29. Adhikary, K., Bhushan, S., & Kumar, S. (2019). Evaluating the performance of various machine learning algorithms for detecting DDOS attacks in vanets. International Journal of Control Automation, 12(5), 478–486.

    Google Scholar 

  30. Agrawal, P. K., Gupta, B. B., & Jain, S. (2011). SVM based scheme for predicting number of zombies in a DDoS attack. Proceeding 2011 European Intelligence and Security Informatics Conference EISIC, 2011, 178–182.

    Google Scholar 

  31. De Farias, G.P.M., De Oliveira, A.L.I., Cabral, G.G., Extreme learning machines for intrusion detection systems. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7666 LNCS, no. PART 4, pp. 535–543, 2012.

  32. Singh, R., Kumar, H., & Singla, R. K. (2015). An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Systems with Applications, 42(22), 8609–8624.

    Google Scholar 

  33. Abusitta, A., Bellaiche, M., & Dagenais, M. (2018). An SVM-based framework for detecting DoS attacks in virtualized clouds under changing environment. Journal of Cloud Computing, 7(1), 1–18.

    Google Scholar 

  34. Sharma, S., & Kaul, A. (2018). A survey on Intrusion detection systems and honeypot based proactive security mechanisms in VANETs and VANET Cloud. Vehicular Communications, 12(April), 138–164.

    Google Scholar 

  35. 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.

    Google Scholar 

  36. Van Der Heijden, R. W., Dietzel, S., Leinmüller, T., & Kargl, F. (2019). Survey on misbehavior detection in cooperative intelligent transportation systems. IEEE Communications Surveys and Tutorials, 21(1), 779–811.

    Google Scholar 

  37. Hui Yang, M., & Chuan Wang, R. (2008). DDoS detection based on wavelet kernel support vector machine. The Journal of China Universities of Posts and Telecommunications, 15(3), 59–63.

    Google Scholar 

  38. Kale, M., & Choudhari, D. M. (2014). DDOS attack detection based on an ensemble of neural classifier. International Journal of Computer Science and Network Security (IJCSNS), 14(7), 122.

    Google Scholar 

  39. Sarkar, B. K., & Sana, S. S. (2009). A hybrid approach to design efficient learning classifiers. Computers and Mathematics with Applications, 58(1), 65–73.

    MathSciNet  MATH  Google Scholar 

  40. Sivatha Sindhu, S. S., Geetha, S., & Kannan, A. (2012). Decision tree based light weight intrusion detection using a wrapper approach. Expert Systems with applications, 39(1), 129–141.

    Google Scholar 

  41. Adhikary, K., Bhushan, S., Kumar, S., & Dutta, K. (2020). Decision tree and neural network based hybrid algorithm for detecting and preventing Ddos attacks in VANETS. International Journal of Innovative Technology and Exploring Engineering, 5, 669–675.

    Google Scholar 

  42. Hosseini, S., & Azizi, M. (2019). The hybrid technique for DDoS detection with supervised learning algorithms. Computer Networks, 158, 35–45.

    Google Scholar 

  43. Sinha, S., Paul, A. (2020) Neuro-fuzzy based intrusion detection system for wireless sensor network. Wireless Personal Communications 1–17

  44. Ravale, U., Marathe, N., & Padiya, P. (2015). Feature selection based hybrid anomaly intrusion detection system using K Means and RBF kernel function. Procedia Computer Science, 45, 428–435.

    Google Scholar 

  45. Sharanya, S., & Karthikeyan, S. (2017). Classifying malicious nodes in VANETs using support vector machines with modified fading memory. ARPN Journal of Engineering and Applied Sciences, 12(1), 171–176.

    Google Scholar 

  46. Hardy, R. L. (1971). Multiquadric equations of topography and other irregular surfaces. Journal of Geophysical Research, 76(8), 1905–1915.

    Google Scholar 

  47. Racine, J. S. (2012). RSTUDIO: A platform-independent IDE for R and sweave. Journal of Applied Econometrics, 27(1), 167–172.

    Google Scholar 

  48. Rana, P. S., Sharma, H., Bhattacharya, M., & Shukla, A. (2015). Quality assessment of modeled protein structure using physicochemical properties. Journal of Bioinformatics and Computational Biology, 13(2), 1550005.

    Google Scholar 

  49. Hand, D. J. (2009). Measuring classifier performance: A coherent alternative to the area under the ROC curve. Machine Learning, 77(1), 103–123.

    Google Scholar 

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Authors and Affiliations

  1. I.K.G. Punjab Technical University, Kapurthala, Punjab, India

    Kaushik Adhikary

  2. Chandigarh Engineering College, Landran, Punjab, India

    Shashi Bhushan

  3. Maharaja Agarsen University, Solan, Himachal Pradesh, India

    Sunil Kumar

  4. National Institute of Technology, Hamirpur, Himachal Pradesh, India

    Kamlesh Dutta

Authors
  1. Kaushik Adhikary
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  2. Shashi Bhushan
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  3. Sunil Kumar
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  4. Kamlesh Dutta
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Correspondence to Kaushik Adhikary.

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Adhikary, K., Bhushan, S., Kumar, S. et al. Hybrid Algorithm to Detect DDoS Attacks in VANETs. Wireless Pers Commun 114, 3613–3634 (2020). https://doi.org/10.1007/s11277-020-07549-y

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  • DOI: https://doi.org/10.1007/s11277-020-07549-y

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