International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
권호 표지
DOI QR코드

DOI QR Code

An Improved Intrusion Detection System for SDN using Multi-Stage Optimized Deep Forest Classifier

  • Received : 2022.04.05
  • Published : 2022.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

Keywords

References

  1. Dong, B., & Wang, X. (2016, June). Comparison deep learning method to traditional methods using for network intrusion detection. In 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN) (pp. 581-585). IEEE.
  2. Zarpelao, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017). A survey of intrusion detection in Internet of Things. Journal of Network and Computer Applications, 84, 25-37. https://doi.org/10.1016/j.jnca.2017.02.009
  3. Mukherjee, B., Heberlein, L. T., & Levitt, K. N. (1994). Network intrusion detection. IEEE network, 8(3), 26-41. https://doi.org/10.1109/65.283931
  4. Wagh, S. K., Pachghare, V. K., & Kolhe, S. R. (2013). Survey on intrusion detection system using machine learning techniques. International Journal of Computer Applications, 78(16).
  5. Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2), 493-501. https://doi.org/10.1007/s12083-017-0630-0
  6. Panda, M., Abraham, A., Das, S., & Patra, M. R. (2011). Network intrusion detection system: A machine learning approach. Intelligent Decision Technologies, 5(4), 347-356. https://doi.org/10.3233/idt-2011-0117
  7. Li, W., Yi, P., Wu, Y., Pan, L., & Li, J. (2014). A new intrusion detection system based on KNN classification algorithm in wireless sensor network. Journal of Electrical and Computer Engineering, 2014.
  8. Garg, S., & Batra, S. (2017). A novel ensembled technique for anomaly detection. International Journal of Communication Systems, 30(11), e3248. https://doi.org/10.1002/dac.3248
  9. Kuang, F., Xu, W., & Zhang, S. (2014). A novel hybrid KPCA and SVM with GA model for intrusion detection. Applied Soft Computing, 18, 178-184. https://doi.org/10.1016/j.asoc.2014.01.028
  10. Wang, W., Zhu, M., Zeng, X., Ye, X., & Sheng, Y. (2017, January). Malware traffic classification using convolutional neural network for representation learning. In 2017 International Conference on Information Networking (ICOIN) (pp. 712-717). IEEE.
  11. Torres, P., Catania, C., Garcia, S., & Garino, C. G. (2016, June). An analysis of recurrent neural networks for botnet detection behavior. In 2016 IEEE biennial congress of Argentina (ARGENCON) (pp. 1-6). IEEE.
  12. Staudemeyer, R. C., & Omlin, C. W. (2013). ACM press the south African institute for computer scientists and information technologists conference-east London south Africa (2013.10. 07-2013.10. 09) proceedings of the south African institute for computer scientists and information technologists co. In Proc. South African Inst. Comput. Scientists Inf. Technol. Conf. (pp. 252-261).
  13. Zhou, Z. H., & Feng, J. (2017). Deep forest: Towards an alternative to deep neural networks. arXiv. arXiv preprint arXiv:1702.08835.
  14. Ustebay, S., Turgut, Z., & Aydin, M. A. (2018, December). Intrusion detection system with recursive feature elimination by using random forest and deep learning classifier. In 2018 international congress on big data, deep learning and fighting cyber terrorism (IBIGDELFT) (pp. 71-76). IEEE.
  15. Xiao, Y., Fan, Z. J., Nayak, A., & Tan, C. X. (2019). Discovery method for distributed denial-of-service attack behavior in SDNs using a feature-pattern graph model. Frontiers of Information Technology & Electronic Engineering, 20(9), 1195-1208. https://doi.org/10.1631/FITEE.1800436
  16. Tuan, N. N., Hung, P. H., Nghia, N. D., Van Tho, N., Phan, T. V., & Thanh, N. H. (2019, October). A Robust TCP-SYN Flood Mitigation Scheme Using Machine Learning Based on SDN. In 2019 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 363-368). IEEE.
  17. Xu, Y., Sun, H., Xiang, F., & Sun, Z. (2019). Efficient DDoS detection based on K-FKNN in software defined networks. IEEE Access, 7, 160536-160545. https://doi.org/10.1109/access.2019.2950945
  18. Mehr, S. Y., & Ramamurthy, B. (2019, December). An SVM based DDoS attack detection method for Ryu SDN controller. In Proceedings of the 15th international conference on emerging networking experiments and technologies (pp. 72-73).
  19. Wang, Y., Hu, T., Tang, G., Xie, J., & Lu, J. (2019). SGS: Safeguard scheme for protecting control plane against DDoS attacks in software-defined networking. IEEE Access, 7, 34699-34710. https://doi.org/10.1109/access.2019.2895092
  20. Fouladi, R. F., Ermis, O., & Anarim, E. (2020). A DDoS attack detection and defense scheme using time-series analysis for SDN. Journal of Information Security and Applications, 54, 102587. https://doi.org/10.1016/j.jisa.2020.102587
  21. Dehkordi, A. B., Soltanaghaei, M., & Boroujeni, F. Z. (2021). The DDoS attacks detection through machine learning and statistical methods in SDN. The Journal of Supercomputing, 77(3), 2383-2415. https://doi.org/10.1007/s11227-020-03323-w
  22. Mishra, A., Gupta, N., & Gupta, B. B. (2021). Defense mechanisms against DDoS attack based on entropy in SDNcloud using POX controller. Telecommunication systems, 77(1), 47-62. https://doi.org/10.1007/s11235-020-00747-w
  23. Shohani, R. B., Mostafavi, S., & Hakami, V. (2021). A Statistical Model for Early Detection of DDoS Attacks on Random Targets in SDN. Wireless Personal Communications, 1-22.
  24. Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE transactions on emerging topics in computational intelligence, 2(1), 41-50. https://doi.org/10.1109/tetci.2017.2772792
  25. Wu, K., Chen, Z., & Li, W. (2018). A novel intrusion detection model for a massive network using convolutional neural networks. Ieee Access, 6, 50850-50859. https://doi.org/10.1109/access.2018.2868993
  26. Vasan, K. K., & Surendiran, B. (2016). Dimensionality reduction using principal component analysis for network intrusion detection. Perspectives in Science, 8, 510-512. https://doi.org/10.1016/j.pisc.2016.05.010
  27. Natesan, P., Rajalaxmi, R. R., Gowrison, G., & Balasubramanie, P. (2017). Hadoop based parallel binary bat algorithm for network intrusion detection. International Journal of Parallel Programming, 45(5), 1194-1213. https://doi.org/10.1007/s10766-016-0456-z
  28. Wei, P., Li, Y., Zhang, Z., Hu, T., Li, Z., & Liu, D. (2019). An optimization method for intrusion detection classification model based on deep belief network. IEEE Access, 7, 87593-87605. https://doi.org/10.1109/access.2019.2925828
  29. Jiang, K., Wang, W., Wang, A., & Wu, H. (2020). Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access, 8, 32464-32476. https://doi.org/10.1109/access.2020.2973730
  30. Zhang, X., Chen, J., Zhou, Y., Han, L., & Lin, J. (2019). A multiple-layer representation learning model for network-based attack detection. IEEE Access, 7, 91992-92008. https://doi.org/10.1109/access.2019.2927465
  31. Yu, Y., & Bian, N. (2020). An intrusion detection method using few-shot learning. IEEE Access, 8, 49730-49740. https://doi.org/10.1109/access.2020.2980136
  32. Xiao, Y., Xing, C., Zhang, T., & Zhao, Z. (2019). An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access, 7, 42210-42219. https://doi.org/10.1109/access.2019.2904620
  33. Ali Alheeti, K. M., & McDonald-Maier, K. (2018). Intelligent intrusion detection in external communication systems for autonomous vehicles. Systems Science & Control Engineering, 6(1), 48-56. https://doi.org/10.1080/21642583.2018.1440260
  34. Chen, Z., Yan, Q., Han, H., Wang, S., Peng, L., Wang, L., & Yang, B. (2018). Machine learning based mobile malware detection using highly imbalanced network traffic. Information Sciences, 433, 346-364. https://doi.org/10.1016/j.ins.2017.04.044
  35. Chen, Z., Yan, Q., Han, H., Wang, S., Peng, L., Wang, L., & Yang, B. (2018). Machine learning based mobile malware detection using highly imbalanced network traffic. Information Sciences, 433, 346-364. https://doi.org/10.1016/j.ins.2017.04.044
  36. Tan, X., Su, S., Huang, Z., Guo, X., Zuo, Z., Sun, X., & Li, L. (2019). Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm. Sensors, 19(1), 203. https://doi.org/10.3390/s19010203
  37. Catalkaya, M. B., Kalipsiz, O., Aktas, M. S., & Turgut, U. O. (2018, September). Data feature selection methods on distributed big data processing platforms. In 2018 3rd International Conference on Computer Science and Engineering (UBMK) (pp. 133-138). IEEE.
  38. Krishna, R. S. B., & Aramudhan, M. (2014, July). Feature selection based on information theory for pattern classification. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 1233-1236). IEEE.
  39. Bonev, B. (2010). Feature selection based on information theory. Universidad de Alicante.
  40. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature selection: A data perspective. ACM Computing Surveys (CSUR), 50(6), 1-45.
  41. Hall, M. A. (1999). Correlation-based feature selection for machine learning.
  42. Moubayed, A., Injadat, M., Shami, A., & Lutfiyya, H. (2018, March). Relationship between student engagement and performance in e-learning environment using association rules. In 2018 IEEE world engineering education conference (EDUNINE) (pp. 1-6). IEEE.
  43. Koch, P., Wujek, B., Golovidov, O., & Gardner, S. (2017). Automated hyperparameter tuning for effective machine learning. In proceedings of the SAS Global Forum 2017 Conference (pp. 1-23). Cary, NC: SAS Institute Inc..
  44. Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295-316. https://doi.org/10.1016/j.neucom.2020.07.061
  45. Bergstra, J., & Bengio, Y. (2012). Random search for hyperparameter optimization. Journal of machine learning research, 13(2).
  46. Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2020). Systematic ensemble model selection approach for educational data mining. Knowledge-Based Systems, 200, 105992. https://doi.org/10.1016/j.knosys.2020.105992
  47. Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2020). Multi-split optimized bagging ensemble model selection for multi-class educational data mining. Applied Intelligence, 50(12), 4506-4528. https://doi.org/10.1007/s10489-020-01776-3
  48. Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2009). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239-287. https://doi.org/10.1007/s11047-008-9098-4
  49. Cohen, G., Hilario, M., & Geissbuhler, A. (2004, November). Model selection for support vector classifiers via genetic algorithms. An application to medical decision support. In International Symposium on Biological and Medical Data Analysis (pp. 200-211). Springer, Berlin, Heidelberg.
  50. Salo, F., Injadat, M., Nassif, A. B., Shami, A., & Essex, A. (2018). Data mining techniques in intrusion detection systems: A systematic literature review. IEEE Access, 6, 56046-56058. https://doi.org/10.1109/access.2018.2872784
  51. Moubayed, A., Refaey, A., & Shami, A. (2019). Softwaredefined perimeter (sdp): State of the art secure solution for modern networks. IEEE network, 33(5), 226-233. https://doi.org/10.1109/mnet.2019.1800324
  52. Kumar, P., Moubayed, A., Refaey, A., Shami, A., & Koilpillai, J. (2019, April). Performance analysis of sdp for secure internal enterprises. In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE.
  53. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009, July). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1-6). IEEE.