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Vol. 7, No. 2, May 2022

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Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient

https://doi.org/10.22219/kinetik.v7i2.1405
Mirza Maulana Azmi
Universitas Muhammadiyah Malang
Fauzi Dwi Setiawan Sumadi
Universitas Muhammadiyah Malang

Corresponding Author(s) : Fauzi Dwi Setiawan Sumadi

fauzisumadi@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 2, May 2022

Abstract

The evolution of computer network technology is now experiencing substantial changes, particularly with the introduction of a new paradigm, Software Defined Networking (SDN). The SDN architecture has been applied in a variety of networks, including the Internet of Things (IoT), which is known as SD-IoT. IoT is made up of billions of networking devices that are interconnected and linked to the Internet. Since the SD-IoT was considered as a complex entity, several types of attack on vulnerabilities vary greatly and can be exploited by careless individuals. Low-Rate Distributed Denial of Service (LRDDoS) is one of the availability-based attack that may affect the SD-IoT integration paradigm. Therefore, it is necessary to have an Intrusion Detection System (IDS) to overcome the security hole caused by LRDDoS. The main objective of this research was the establishment of an IDS application for resolving LRDDoS attack using the SVM algorithm combined with the Feature Importance method, namely the Logistic Regression Coefficient. The implemented approach was developed to reduce the complexity or resource’s consumption during the classification process as well as increasing the accuracy. It could be concluded that the Linear kernel SVM algorithm acquired the highest results on the test schemes at 100% accuracy, but the training time required for this model was longer, about 23.6 seconds compared to the Radial Basis Function model which only takes about 1.5 seconds.

Keywords

Low-Rate Attack DDoS SD-IoT SVM IDS
Azmi, M. M., & Sumadi, . F. D. S. (2022). Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(2). https://doi.org/10.22219/kinetik.v7i2.1405
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References
  1. F. D. Setiawan Sumadi and C. S. Kusuma Aditya, “Comparative Analysis of DDoS Detection Techniques Based on Machine Learning in OpenFlow Network,” in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Dec. 2020, pp. 152–157, doi: https://doi.org/10.1109/ISRITI51436.2020.9315510.
  2. Kilwalaga, I. F., Sumadi, F. D. S., & Syaifuddin, S. (2020). SDN-Honeypot Integration for DDoS Detection Scheme Using Entropy. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(3), 187-194. https://doi.org/10.22219/kinetik.v5i3.1058.
  3. K. Nisar et al., “A Survey on The Architecture, Application, and Security of Software Defined Networking: Challenges and Open Issues,” Internet of Things, vol. 12, p. 100289, Dec. 2020, doi: https://doi.org/10.1016/j.iot.2020.100289.
  4. M. Alsaeedi, M. M. Mohamad, and A. A. Al-Roubaiey, “Toward Adaptive and Scalable OpenFlow-SDN Flow Control: A Survey,” IEEE Access, vol. 7, pp. 107346–107379, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2932422.
  5. T. Li, J. Chen, and H. Fu, “Application Scenarios based on SDN: An Overview,” J. Phys. Conf. Ser., vol. 1187, no. 5, p. 052067, Apr. 2019, doi: https://doi.org/10.1088/1742-6596/1187/5/052067.
  6. L. Ben Azzouz and I. Jamai, “SDN, Slicing, and NFV Paradigms for A Smart Home: A Comprehensive Survey,” Trans. Emerg. Telecommun. Technol., vol. 30, no. 10, pp. 1–13, Oct. 2019, doi: https://doi.org/10.1002/ett.3744.
  7. P. P. Ray and N. Kumar, “SDN/NFV Architectures for Edge-Cloud Oriented IoT: A Systematic Review,” Comput. Commun., vol. 169, no. June 2020, pp. 129–153, Mar. 2021, doi: https://doi.org/10.1016/j.comcom.2021.01.018.
  8. D. Yin, L. Zhang, and K. Yang, “A DDoS Attack Detection and Mitigation With Software-Defined Internet of Things Framework,” IEEE Access, vol. 6, no. Mcc, pp. 24694–24705, 2018, doi: https://doi.org/10.1109/ACCESS.2018.2831284.
  9. H. Cheng, J. Liu, T. Xu, B. Ren, J. Mao, and W. Zhang, “Machine Learning Based Low-Rate DDoS Attack Detection For SDN Enabled Iot Networks,” Int. J. Sens. Networks, vol. 34, no. 1, p. 56, 2020, doi: https://doi.org/10.1504/IJSNET.2020.109720.
  10. S. Xie, C. Xing, G. Zhang, and J. Zhao, “A Table Overflow LDoS Attack Defending Mechanism in Software-Defined Networks,” Secur. Commun. Networks, vol. 2021, pp. 1–16, Jan. 2021, doi: https://doi.org/10.1155/2021/6667922.
  11. O. Gugi Housman, H. Isnaini, and F. Sumadi, “SDN-DDOS (ICMP,TCP,UDP).” Mendeley Data, p. V1, 2020, doi: https://doi.org/10.17632/hkjbp67rsc.1.
  12. D. Y. Setiawan, S. Naning Hertiana, and R. M. Negara, “6LoWPAN Performance Analysis of IoT Software-Defined-Network-Based Using Mininet-Io,” in 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), Jan. 2021, pp. 60–65, doi: https://doi.org/10.1109/IoTaIS50849.2021.9359714.
  13. S. Asadollahi, B. Goswami, and M. Sameer, “Ryu Controller’s Scalability Experiment on Software Defined Networks,” in 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Feb. 2018, pp. 1–5, doi: https://doi.org/10.1109/ICCTAC.2018.8370397.
  14. M. Ushakova, Y. Ushakov, J. Cui, L. Legashev, A. Shukhman, and A. Bolodurin, “Research of Performance Parameters of Virtual Switches with OpenFlow Support,” in 2020 International Conference Engineering and Telecommunication (En&T), Nov. 2020, pp. 1–4, doi: https://doi.org/10.1109/EnT50437.2020.9431289.
  15. R. Wazirali, R. Ahmad, and S. Alhiyari, “SDN-OpenFlow Topology Discovery: An Overview of Performance Issues,” Appl. Sci., vol. 11, no. 15, p. 6999, Jul. 2021, doi: https://doi.org/10.3390/app11156999.
  16. E. Al-Masri et al., “Investigating Messaging Protocols for the Internet of Things (IoT),” IEEE Access, vol. 8, pp. 94880–94911, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2993363.
  17. J. Singh and S. Behal, “Detection and Mitigation of DDoS Attacks in SDN: A Comprehensive Review, Research Challenges and Future Directions,” Comput. Sci. Rev., vol. 37, p. 100279, Aug. 2020, doi: https://doi.org/10.1016/j.cosrev.2020.100279.
  18. J. Sengupta, S. Ruj, and S. Das Bit, “A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT,” J. Netw. Comput. Appl., vol. 149, p. 102481, Jan. 2020, doi: https://doi.org/10.1016/j.jnca.2019.102481.
  19. Open Networking Foundation, “OpenFlow Switch Specification (Version 1.5.1),” Current, vol. 0, pp. 1–36, 2015, [Online]. Available: https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf-specifications/openflow/openflow-switch-v1.5.1.pdf.
  20. M. P. Singh and A. Bhandari, “New-flow Based DDoS Attacks in SDN: Taxonomy, Rationales, and Research Challenges,” Comput. Commun., vol. 154, no. October 2019, pp. 509–527, Mar. 2020, doi: https://doi.org/10.1016/j.comcom.2020.02.085.
  21. U. M. Khaire and R. Dhanalakshmi, “Stability of Feature Selection Algorithm: A Review,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, Jun. 2019, doi: https://doi.org/10.1016/j.jksuci.2019.06.012.
  22. A. A. Megantara and T. Ahmad, “Feature Importance Ranking for Increasing Performance of Intrusion Detection System,” in 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE), Sep. 2020, pp. 37–42, doi: https://doi.org/10.1109/IC2IE50715.2020.9274570.
  23. X. Zou, Y. Hu, Z. Tian, and K. Shen, “Logistic Regression Model Optimization and Case Analysis,” in 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), Oct. 2019, pp. 135–139, doi: https://doi.org/10.1109/ICCSNT47585.2019.8962457.
  24. E. Y. Boateng and D. A. Abaye, “A Review of the Logistic Regression Model with Emphasis on Medical Research,” J. Data Anal. Inf. Process., vol. 07, no. 04, pp. 190–207, 2019, doi: https://doi.org/10.4236/jdaip.2019.74012.
  25. H. Hemasinghe, R. S. S. Rangali, N. L. Deshapriya, and L. Samarakoon, “Landslide Susceptibility Mapping Using Logistic Regression Model (A Case Study in Badulla District, Sri Lanka),” Procedia Eng., vol. 212, pp. 1046–1053, 2018, doi: https://doi.org/10.1016/j.proeng.2018.01.135.
  26. G. Eraslan, Ž. Avsec, J. Gagneur, and F. J. Theis, “Deep Learning: New Computational Modelling Techniques for Genomics,” Nat. Rev. Genet., vol. 20, no. 7, pp. 389–403, Jul. 2019, doi: https://doi.org/10.1038/s41576-019-0122-6.
  27. G. Heinze, C. Wallisch, and D. Dunkler, “Variable Selection - A Review and Recommendations for The Practicing Statistician,” Biometrical J., vol. 60, no. 3, pp. 431–449, May 2018, doi: https://doi.org/10.1002/bimj.201700067.
  28. M. E. Shipe, S. A. Deppen, F. Farjah, and E. L. Grogan, “Developing Prediction Models for Clinical Use Using Logistic Regression: An Overview,” J. Thorac. Dis., vol. 11, no. S4, pp. S574–S584, Mar. 2019, doi: https://doi.org/10.21037/jtd.2019.01.25.
  29. C. Gambella, B. Ghaddar, and J. Naoum-Sawaya, “Optimization Problems for Machine Learning: A Survey,” Eur. J. Oper. Res., vol. 290, no. 3, pp. 807–828, May 2021, doi: https://doi.org/10.1016/j.ejor.2020.08.045.
  30. M. A. Aladaileh, M. Anbar, I. H. Hasbullah, Y.-W. Chong, and Y. K. Sanjalawe, “Detection Techniques of Distributed Denial of Service Attacks on Software-Defined Networking Controller–A Review,” IEEE Access, vol. 8, pp. 143985–143995, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3013998.
  31. B. Isyaku, M. S. Mohd Zahid, M. Bte Kamat, K. Abu Bakar, and F. A. Ghaleb, “Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey,” Futur. Internet, vol. 12, no. 9, p. 147, Aug. 2020, doi: https://doi.org/10.3390/fi12090147.
  32. S. Kotey, E. Tchao, and J. Gadze, “On Distributed Denial of Service Current Defense Schemes,” Technologies, vol. 7, no. 1, p. 19, Jan. 2019, doi: https://doi.org/10.3390/technologies7010019.
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