73
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Multi-classifier and meta-heuristic based cache pollution attacks and interest flooding attacks detection and mitigation model for named data networking

&
Pages 839-864 | Received 26 Jul 2021, Accepted 13 Aug 2022, Published online: 26 Sep 2022

References

  • Ahmed, S. H., & Kim, D. (2016). Named data networking-based smart home. Ict Express, 2(3), 130–134. https://doi.org/10.1016/j.icte.2016.08.007
  • Alhisnawi, M., & Ahmadi, M. (2020). Detecting and mitigating DDoS attack in named data networking. Journal of Network and Systems Management, 28(4), 1343–1365 doi:10.1016/j.jnca.2017.11.002. https://doi.org/10.1007/s10922-020-09539-8
  • Alubady, R., Hassan, S., & Habbal, A. (2018). Pending interest table control management in Named Data Network. Journal of Network and Computer Applications, 111, 99–116.
  • Benarfa, A., Hassan, M., Losiouk, E., Compagno, A., Yagoubi, M. B., & Conti, M. (2021). ChoKIFA+: An early detection and mitigation approach against interest flooding attacks in NDN. International Journal of Information Security, 20(3), 269–285 doi:10.1007/s10207-020-00500-z.
  • Bian, C., Zhao, T., Li, X., & Yan, W. (2015). Boosting named data networking for data dissemination in urban VANET scenarios. Vehicular Communications, 2(4), 195–207. https://doi.org/10.1016/j.vehcom.2015.08.001
  • Binu, D., & Kariyappa, B. S. (2018). RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. Ieee Transactions on Instrumentation and Measurement, 68(1), 2–26. https://doi.org/10.1109/TIM.2018.2836058
  • Conti, M., Gasti, P., & Teoli, M. (2013). A lightweight mechanism for detection of cache pollution attacks in named data networking. Computer Networks, 57(16), 3178–3191. https://doi.org/10.1016/j.comnet.2013.07.034
  • Duarte, J. M., Braun, T., & Villas, L. A. (2019). MobiVNDN: A distributed framework to support mobility in vehicular named-data networking. Ad Hoc Networks, 82, 77–90. https://doi.org/10.1016/j.adhoc.2018.08.008
  • Gandomi, A. H., Yang, X. S., Talatahari, S., & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science & Numerical Simulation, 18(1), 89–98. https://doi.org/10.1016/j.cnsns.2012.06.009
  • Guo, H., Wang, X., Chang, K., & Tian, Y. (2016). Exploiting path diversity for thwarting pollution attacks in named data networking. Ieee Transactions on Information Forensics and Security, 11(9), 2077–2090. https://doi.org/10.1109/TIFS.2016.2574307
  • Hou, R., Han, M., Chen, J., Hu, W., Tan, X., Luo, J., & Ma, M. (2019). Theil-Based countermeasure against interest flooding attacks for named data networks. Ieee Network, 33(3), 116–121 doi:10.1109/MNET.2019.1800350.
  • Iqbal, S. M. A. (2018). Adaptive forwarding strategies to reduce redundant interests and data in named data networks. Journal of Network and Computer Applications, 106, 33–47 doi:10.1016/j.jnca.2018.01.013.
  • Kalghoum, A., Gammar, S. M., & Saidane, L. A. (2018). Towards a novel cache replacement strategy for named data networking based on software defined networking. Computers & Electrical Engineering, 66, 98–113 doi:10.1016/j.compeleceng.2017.12.025.
  • Karami, A. (2015). Accpndn: Adaptive congestion control protocol in named data networking by learning capacities using optimized time-lagged feedforward neural network. Journal of Network and Computer Applications, 56, 1–18 doi:10.1016/j.jnca.2015.05.017.
  • Karami, A., & Guerrero-Zapata, M. (2015a). An anfis-based cache replacement method for mitigating cache pollution attacks in named data networking. Computer Networks, 80, 51–65.
  • Karami, A., & Guerrero-Zapata, M. (2015b). A hybrid multiobjective rbf-pso method for mitigating dos attacks in named data networking. Neurocomputing, 151, 1262–1282 doi:10.1016/j.neucom.2014.11.003.
  • Kim, Y., Kim, Y., Bi, J., & Yeom, I. (2016). Differentiated forwarding and caching in named-data networking. Journal of Network and Computer Applications, 60, 155–169 doi:10.1016/j.comnet.2015.01.020.
  • Kokila, R. T., Selvi, S. T., & Govindarajan, K. (2014). DDoS detection and analysis in SDN-based environment using support vector machine classifier. In 2014 sixth international conference on advanced computing (ICoAC) (pp. 205–210). IEEE.
  • Kumar, N., Singh, A. K., & Srivastava, S. (2017). Evaluating machine learning algorithms for detection of interest flooding attack in named data networking. In Proceedings of the 10th International Conference on Security of Information and Networks (pp. 299–302).
  • Li, Q., Zhang, X., Zheng, Q., Sandhu, R., & Fu, X. (2014). Live: Lightweight integrity verification and content access control for named data networking. IEEE Transactions on Information Forensics and Security, 10(2), 308–320 doi:10.1109/TIFS.2014.2365742.
  • Li, Q., Zhao, Z., Xu, M., Jiang, Y., & Yang, Y. (2017). A smart routing scheme for named data networks. Computer Communications, 103, 83–93 doi:10.1016/j.comcom.2016.09.012.
  • Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67 doi:10.1016/j.advengsoft.2016.01.008.
  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61 doi:10.1016/j.advengsoft.2016.01.008.
  • Mun, J. H., & Lim, H. (2017). Cache sharing using bloom filters in named data networking. Journal of Network and Computer Applications, 90, 74–82 doi:10.1016/j.jnca.2017.04.011.
  • O’Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
  • Polydouri, A., Vathi, E., Siolas, G., & Stafylopatis, A. (2020). An efficient classification approach in imbalanced datasets for intrinsic plagiarism detection. Evolving Systems, 11(3), 503–515.
  • Prasanalakshmi, B., & Farouk, A. (2019). Classification and prediction of student academic performance in king khalid university-a machine learning approach. Indian Journal of Science and Technology, 12, 14.
  • Qiao, X., Ren, P., Chen, J., Tan, W., Blake, M. B., & Xu, W. (2019). Session persistence for dynamic web applications in Named Data Networking. Journal of Network and Computer Applications, 125, 220–235.
  • Rajakumar, B. R. (2013). Impact of static and adaptive mutation techniques on the performance of genetic algorithm. International Journal of Hybrid Intelligent Systems, 10(1), 11–22.
  • Ren, Y., Li, J., Shi, S., Li, L., Wang, G., & Zhang, B. (2016). Congestion control in named data networking–a survey. Computer Communications, 86, 1–11.
  • Rezaeifar, Z., Wang, J., & Oh, H. (2018). A trust-based method for mitigating cache poisoning in name data networking. Journal of Network and Computer Applications, 104, 117–132.
  • Roy, R. G., & Ghoshal, D. (2020). Grey wolf optimization-based second order sliding mode control for inchworm robot. Robotica, 38(9), 1539–1557.
  • Salah, H., & Strufe, T. (2016). Evaluating and mitigating a collusive version of the interest flooding attack in NDN. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 938–945). IEEE.
  • Saxena, D., & Raychoudhury, V. (2016). Radient: Scalable, memory efficient name lookup algorithm for named data networking. Journal of Network and Computer Applications, 63, 1–13.
  • Saxena, D., Raychoudhury, V., Suri, N., Becker, C., & Cao, J. (2016). Named data networking: A survey. Computer Science Review, 19, 15–55.
  • Seo, J., & Lim, H. (2018). Bitmap-Based priority-NPT for packet forwarding at named data network. Computer Communications, 130, 101–112.
  • Shaaban, A. R., Abd Elwanis, E., & Hussein, M. (2019). DDoS attack detection and classification via Convolutional Neural Network (CNN). In 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) (pp. 233–238). IEEE.
  • Torres, J. V., Alvarenga, I. D., Boutaba, R., & Duarte, O. C. M. (2017). An autonomous and efficient controller-based routing scheme for networking Named-Data mobility. Computer Communications, 103, 94–103.
  • Tsekouras, G. E., & Tsimikas, J. (2013). On training RBF neural networks using input–output fuzzy clustering and particle swarm optimization. Fuzzy Sets and Systems, 221, 65–89.
  • Vinolin, V. (2019). Breast cancer detection by optimal classification using GWO algorithm. Multimedia Research, 2(2), 10–18.
  • Xue, J., Wu, C., Chen, Z., Van Gelder, P. H. A. J. M., & Yan, X. (2019). Modeling human-like decision-making for inbound smart ships based on fuzzy decision trees. Expert Systems with Applications, 115, 172–188.
  • Yao, J., Yin, B., & Tan, X. (2018). A smdp-based forwarding scheme in named data networking. Neurocomputing, 306, 213–225.
  • Yao, J., Yin, B., Tan, X., & Jiang, X. (2017). A pomdp framework for forwarding mechanism in named data networking. Computer Networks, 112, 167–175.
  • Ye, Y., Lee, B., Flynn, R., Murray, N., Fang, G., Cao, J., & Qiao, Y. (2018). Ptp: Path-specified transport protocol for concurrent multipath transmission in named data networks. Computer Networks, 144, 280–296.
  • Zhang, Z., Wang, T., & Liu, X. (2014). Melt index prediction by aggregated RBF neural networks trained with chaotic theory. Neurocomputing, 131, 368–376.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.