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Intrusion Detection Study and Enhancement Using Machine Learning

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Risks and Security of Internet and Systems (CRiSIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12026))

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Abstract

IoT is an emerging technology, which represents a complex and heterogeneous environment. Thus, security in IoT could be an issue of concern, in particular detecting and identifying malicious events. Malicious events are triggered when anomalous traffic attempts to threaten and abuse the IoT network. Machine learning approaches provide interesting tools to detect new attacks and prevent unauthorized access. Therefore, the aim of this paper is to investigate and compare the performances of the classical machine learning methods: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and K-means. The performance metrics considered in this study are Accuracy, Detection Rate, False Alarm Rate, Recall, Precision, F1- Score, Time Training and Time Assigned Label. Then, a proposed solution for enhancement is elaborated by leveraging the multi-level tweak. The proposed solution shows the best performance results compared to classical machine learning methods for intrusion detection.

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References

  1. Ahmad, I., Basheri, M., Iqbal, M., Raheem, A.: Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6, 33789–33795 (2018)

    Article  Google Scholar 

  2. Al-Yaseen, W.L., Othman, Z.A., Nazri, M.Z.A.: Multi-level hybrid support vector machine and extreme learning machine based on modified k-means for intrusion detection system. Exp. Syst. Appl. 67, 296–303 (2017)

    Article  Google Scholar 

  3. Aljawarneh, S., Aldwairi, M., Yassein, M.B.: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J. Comput. Sci. 25, 152–160 (2018)

    Article  Google Scholar 

  4. Ashraf, N., Ahmad, W., Ashraf, R.: A comparative study of data mining algorithms for high detection rate in intrusion detection system (2018)

    Google Scholar 

  5. Boutaba, R., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 16 (2018)

    Article  Google Scholar 

  6. Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutorials 18(2), 1153–1176 (2016)

    Article  Google Scholar 

  7. Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., Faruki, P.: Network intrusion detection for IoT security based on learning techniques. IEEE Commun. Surv. Tutorials 21, 2671–2701 (2019)

    Article  Google Scholar 

  8. Chae, H.S., Choi, S.: Feature selection for efficient intrusion detection using attribute ratio. Int. J. Comput. Commun. 8, 1–6 (2014)

    Google Scholar 

  9. Chand, N., Mishra, P., Krishna, C.R., Pilli, E.S., Govil, M.C.: A comparative analysis of SVM and its stacking with other classification algorithm for intrusion detection. In: International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Spring), pp. 1–6. IEEE (2016)

    Google Scholar 

  10. da Costa, K.A., Papa, J.P., Lisboa, C.O., Munoz, R., de Albuquerque, V.H.C.: Internet of Things: a survey on machine learning-based intrusion detection approaches. Comput. Netw. 151, 147–157 (2019)

    Article  Google Scholar 

  11. Deep, S., Zheng, X., Hamey, L.: A survey of security and privacy issues in the Internet of Things from the layered context. arXiv preprint arXiv:1903.00846 (2019)

  12. Doshi, R., Apthorpe, N., Feamster, N.: Machine learning DDoS detection for consumer Internet of Things devices. arXiv preprint arXiv:1804.04159 (2018)

  13. Enache, A.C., Sgârciu, V.: Anomaly intrusions detection based on support vector machines with an improved bat algorithm. In: 2015 20th International Conference on Control Systems and Computer Science (CSCS), pp. 317–321. IEEE (2015)

    Google Scholar 

  14. Faria, M.M., Monteiro, A.M.: Intrusion detection in computer networks based on KNN, K-Means++ and J48. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2018. AISC, vol. 868, pp. 256–271. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01054-6_19

    Chapter  Google Scholar 

  15. Haq, N.F., Onik, A.R., Hridoy, M.A.K., Rafni, M., Shah, F.M., Farid, D.M.: Application of machine learning approaches in intrusion detection system: a survey. IJARAI-Int. J. Adv. Res. Artif. Intell. 4(3), 9–18 (2015)

    Google Scholar 

  16. Kaur, K.: A survey on Internet of Things-architecture, applications, and future trends. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), pp. 581–583. IEEE (2018)

    Google Scholar 

  17. Kumar, B.N., Raju, M.S.S.B., Vardhan, B.V.: Enhancing the performance of an intrusion detection system through multi-linear dimensionality reduction and multi-class SVM. Int. J. Intell. Eng. Syst. 11(1), 181–190 (2018)

    Google Scholar 

  18. Li, Y.: Generalization of k-means related algorithms. arXiv preprint arXiv:1903.10025 (2019)

  19. Miller, N.J.: Benchmarks for evaluating anomaly-based intrusion detection solutions. Ph.D. thesis, California State University, Long Beach (2018)

    Google Scholar 

  20. Mishra, P., Varadharajan, V., Tupakula, U., Pilli, E.S.: A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun. Surv. Tutorials 21(1), 686–728 (2018)

    Article  Google Scholar 

  21. Mohamad Tahir, H., et al.: Hybrid machine learning technique for intrusion detection system (2015)

    Google Scholar 

  22. Mohamed, H., Hefny, H., Alsawy, A.: Intrusion detection system using machine learning approaches. Egypt. Comput. Sci. J. 42(3), 1–13 (2018)

    Google Scholar 

  23. Nisioti, A., Mylonas, A., Yoo, P.D., Katos, V.: From intrusion detection to attacker attribution: a comprehensive survey of unsupervised methods. IEEE Commun. Surv. Tutorials 20, 3369–3388 (2018)

    Article  Google Scholar 

  24. Nkiama, H., Said, S.Z.M., Saidu, M.: A subset feature elimination mechanism for intrusion detection system. Int. J. Adv. Comput. Sci. Appl. 7(4), 148–157 (2016)

    Google Scholar 

  25. NSL-KDD: NSL-KDD data set for network-based intrusion detection systems. http://nsl.cs.unb.ca/NSL-KDD/

  26. Özgür, A., Erdem, H.: A review of kdd99 dataset usage in intrusion detection and machine learning between 2010 and 2015. PeerJ PrePrints 4, e1954v1 (2016)

    Google Scholar 

  27. Protić, D.D.: Review of kdd cup’99, nsl-kdd and kyoto 2006+ datasets. Vojnotehnički glasnik 66(3), 580–596 (2018)

    Article  Google Scholar 

  28. Roman, R., Zhou, J., Lopez, J.: On the features and challenges of security and privacy in distributed Internet of Things. Comput. Netw. 57(10), 2266–2279 (2013)

    Article  Google Scholar 

  29. Santos, L., Rabadao, C., Gonçalves, R.: Intrusion detection systems in Internet of Things: a literature review. In: 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–7. IEEE (2018)

    Google Scholar 

  30. Sree, S.B.: Kernel based intrusion detection using data mining techniques (2018)

    Google Scholar 

  31. Teng, S., Wu, N., Zhu, H., Teng, L., Zhang, W.: SVM-DT-based adaptive and collaborative intrusion detection. IEEE/CAA J. Automatica Sinica 5(1), 108–118 (2018)

    Article  Google Scholar 

  32. Tharwat, A.: Parameter investigation of support vector machine classifier with kernel functions. Knowl. Inf. Syst. 61, 1–34 (2019)

    Article  Google Scholar 

  33. Wang, H., Gu, J., Wang, S.: An effective intrusion detection framework based on SVM with feature augmentation. Knowl.-Based Syst. 136, 130–139 (2017)

    Article  Google Scholar 

  34. Xin, Y., et al.: Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365–35381 (2018)

    Article  Google Scholar 

  35. Zhang, S., Cheng, D., Deng, Z., Zong, M., Deng, X.: A novel KNN algorithm with data-driven k parameter computation. Pattern Recogn. Lett. 109, 44–54 (2018)

    Article  Google Scholar 

  36. Zhao, W.L., Deng, C.H., Ngo, C.W.: K-means: a revisit. Neurocomputing 291, 195–206 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory of Technology and Smart Systems (LT2S), Digital Research Center of Sfax (CRNS), University of Sfax, Sfax, Tunisia

    Hela Mliki, Abir Hadj Kaceam & Lamia Chaari

  2. Science Faculty of Gabes, University of Gabes, Gabes, Tunisia

    Hela Mliki

  3. Higher Institute of Computer Science and Multimedia of Sfax, University of Sfax, Sfax, Tunisia

    Abir Hadj Kaceam & Lamia Chaari

Authors
  1. Hela Mliki
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  2. Abir Hadj Kaceam
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  3. Lamia Chaari
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Corresponding author

Correspondence to Hela Mliki .

Editor information

Editors and Affiliations

  1. ReDCAD, University of Sfax, Sfax, Tunisia

    Slim Kallel

  2. IMT Atlantique, Lab-STICC, Université Bretagne Loire, Rennes, France

    Frédéric Cuppens

  3. IMT Atlantique, Lab-STICC, Université Bretagne Loire, Rennes, France

    Nora Cuppens-Boulahia

  4. ReDCAD, University of Sfax, Sfax, Tunisia

    Ahmed Hadj Kacem

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Mliki, H., Kaceam, A.H., Chaari, L. (2020). Intrusion Detection Study and Enhancement Using Machine Learning. In: Kallel, S., Cuppens, F., Cuppens-Boulahia, N., Hadj Kacem, A. (eds) Risks and Security of Internet and Systems. CRiSIS 2019. Lecture Notes in Computer Science(), vol 12026. Springer, Cham. https://doi.org/10.1007/978-3-030-41568-6_17

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  • DOI: https://doi.org/10.1007/978-3-030-41568-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41567-9

  • Online ISBN: 978-3-030-41568-6

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