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Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection

  • 1194: Secured and Efficient Convergence of Artificial Intelligence and Internet of Things
  • Published:
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Abstract

The rapid advancement of technologies has enabled businesses to carryout their activities seamlessly and revolutionised communications across the globe. There is a significant growth in the amount and complexity of Internet of Things devices that are deployed in a wider range of environments. These devices mostly communicate through Wi-Fi networks and particularly in smart environments. Besides the benefits, these devices also introduce security challenges. In this paper, we investigate and leverage effective feature selection techniques to improve intrusion detection using machine learning methods. The proposed approach is based on a centralised intrusion detection system, which uses the deep feature abstraction, feature selection and classification to train the model for detecting the malicious and anomalous actions in the traffic. The deep feature abstraction uses deep learning techniques of artificial neural network in the form of unsupervised autoencoder to construct more features for the traffic. Based on the availability of cumulative features, the system then employs a variety of wrapper-based feature selection techniques ranging from SVM and decision tree to Naive Bayes for selecting high-ranked features, which are then combined and fed into an artificial neural network classifier for distinguishing attack and normal behaviors. The experimental results reveal the effectiveness of the proposed method on Aegean Wi-Fi Intrusion Dataset, which achieves high detection accuracy of up to 99.95%, relatively competitive to the existing machine learning works for the same dataset.

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References

  1. Alamiedy TA, Anbar M, Alqattan ZNM, Alzubi QM (2020) Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. J Amb Intell Hum Comput 11(9):3735–3756

    Article  Google Scholar 

  2. Al-Jarrah OY, Alhussein O, Yoo PD, Muhaidat S, Taha K, Kim K (2016) Data randomization and cluster-based partitioning for botnet intrusion detection. IEEE Trans Cybern 46(8):1796–1806

    Article  Google Scholar 

  3. Aminanto ME, Choi R, Tanuwidjaja HC, Yoo PD, Kim K (2018) Deep abstraction and weighted feature selection for Wi-Fi impersonation detection. IEEE Trans Inf Foren Sec 13(3):621–636

    Article  Google Scholar 

  4. Bhosale KS, Nenova M, Iliev G (2018) Modified naive bayes intrusion detection system (MNBIDS). In: 2018 international conference on computational techniques, electronics and mechanical systems (CTEMS). IEEE, pp 291–296

  5. Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Topics Appl Earth Observ Remote Sens 8(6):2381–2392

    Article  Google Scholar 

  6. Chiew KL, Tan CL, Wong K, Yong KS, Tiong WK (2019) A new hybrid ensemble feature selection framework for machine learning-based phishing detection system. Inf Sci 484:153–166

    Article  Google Scholar 

  7. Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, Hoboken

    MATH  Google Scholar 

  8. Deng L, Li D, Yao X, Cox D, Wang H (2019) Mobile network intrusion detection for iot system based on transfer learning algorithm. Clust Comput 22(4):9889–9904

    Article  Google Scholar 

  9. Elgammal MA, Mostafa H, Salama KN, Nader Mohieldin A (2019) A comparison of artificial neural network(ANN) and support vector machine(SVM) classifiers for neural seizure detection. In: 2019 IEEE 62nd international midwest symposium on circuits and systems (MWSCAS). IEEE, pp 646–649

  10. Firdaus A, Anuar NB, Karim A, Razak MFA (2018) Discovering optimal features using static analysis and a genetic search based method for Android malware detection. Front Inf Technol Elec Eng 19(6):712–736

    Article  Google Scholar 

  11. Gao X, Shan C, Hu C, Niu Z, Liu Z (2019) An adaptive ensemble machine learning model for intrusion detection. IEEE Access 7:82512–82521

    Article  Google Scholar 

  12. Gassais R, Ezzati-Jivan N, Fernandez JM, Aloise D, Dagenais MR (2020) Multi-level host-based intrusion detection system for internet of things. J Cloud Comput 9(1):1–16

    Article  Google Scholar 

  13. Gul A, Adali E (2017) A feature selection algorithm for IDS. In: 2017 international conference on computer science and engineering (UBMK). IEEE, pp 816–820

  14. Han S, Xie M, Chen HH, Ling Y (2014) Intrusion detection in cyber-physical systems: techniques and challenges. IEEE Sys J 8(4):1052–1062

    Article  Google Scholar 

  15. He D, Liu X, Zheng J, Chan S, Zhu S, Min W, Guizani N (2020) A lightweight and intelligent intrusion detection system for integrated electronic systems. IEEE Netw 34(4):173–179

    Article  Google Scholar 

  16. Jaber AN, Rehman SU (2020) FCM–SVM Based intrusion detection system for cloud computing environment. Cluster Comput

  17. Jan SU, Ahmed S, Shakhov V, Koo I (2019) Toward a lightweight intrusion detection system for the internet of things. IEEE Access 7:42450–42471

    Article  Google Scholar 

  18. Javadpour A, Kazemi Abharian S, Wang G (2017) Feature selection and intrusion detection in cloud environment based on machine learning algorithms. In: 2017 IEEE International symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC). IEEE, pp 1417–1421

  19. Kasongo SM, Sun Y (2019) A deep learning method with filter based feature engineering for wireless intrusion detection system. IEEE Access 7:38597–38607

    Article  Google Scholar 

  20. Kasongo SM, Sun Y (2020) A deep learning method with wrapper based feature extraction for wireless intrusion detection system. Comput Secur 92:101752

    Article  Google Scholar 

  21. Kasun LLC, Yang Y, Huang GB, Zhang Z (2016) Dimension reduction with extreme learning machine. IEEE Trans Image Process 25(8):3906–3918

    Article  MathSciNet  Google Scholar 

  22. Kolias C, Kambourakis G, Stavrou A, Gritzalis S (2016) Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Commun Surv Tutor 18(1):184–208

    Article  Google Scholar 

  23. Lee SJ, Yoo PD, Asyhari AT, Jhi Y, Chermak L, Yeun CY, Taha K (2020) Impact: impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access 8:65520–65529

    Article  Google Scholar 

  24. Li Y, Xu Y, Liu Z, Hou H, Zheng Y, Xin Y, Zhao Y, Cui L (2020) Robust detection for network intrusion of industrial iot based on multi-cnn fusion. Measurement 154:107450

    Article  Google Scholar 

  25. Mighan SN, Kahani M (2020) A novel scalable intrusion detection system based on deep learning. Int J Inf Secur: 1–17

  26. Mu Y, Liu X, Yang Z, Liu X (2017) A parallel C4.5 decision tree algorithm based on MapReduce. Concur Comput Prac Exp 29(8):e4015

    Article  Google Scholar 

  27. Omrani T, Dallali A, Rhaimi BC, Fattahi J (2017) Fusion of ANN and SVM classifiers for network attack detection. In: 2017 18th international conference on sciences and techniques of automatic control and computer engineering (STA). IEEE, pp 374–377

  28. Ouyang W, Zhou H, Li H, Li Q, Yan J, Wang X (2018) Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection. IEEE Trans Patt Anal Mach Intell 40(8):1874–1887

    Article  Google Scholar 

  29. Parker LR, Yoo PD, Asyhari TA, Chermak L, Jhi Y, Taha K (2019) DEMISE. In: Proceedings of the 14th international conference on availability, reliability and security - ARES ’19. ACM Press, New York, pp 1–10

  30. Phan AV, Nguyen ML, Bui LT (2017) Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems. Appl Intell 46(2):455–469

    Article  Google Scholar 

  31. Rahman MA, Asyhari AT (2019) The emergence of internet of things (iot): Connecting anything, anywhere. Computers 8(2):40

    Article  Google Scholar 

  32. Rahman MA, Asyhari AT, Leong L, Satrya G, Hai Tao M, Zolkipli M (2020) Scalable machine learning-based intrusion detection system for iot-enabled smart cities. Sustain Cities Soc 61:102324

    Article  Google Scholar 

  33. Sahani R, Shatabdinalini, Rout C, Chandrakanta Badajena J, Jena AK, Das H (2018) Classification of intrusion detection using data mining techniques: 753–764

  34. Salih AA, Abdulrazaq MB (2019) Combining best features selection using three classifiers in intrusion detection system. In: 2019 International conference on advanced science and engineering (ICOASE). IEEE, pp 94–99

  35. Sheen S, Anitha R, Natarajan V (2015) Android based malware detection using a multifeature collaborative decision fusion approach. Neurocomputing 151:905–912

    Article  Google Scholar 

  36. Singh D, Roy D, Mohan CK (2017) Dip-SVM : distribution preserving kernel support vector machine for big data. IEEE Trans Big Data 3(1):79–90

    Article  Google Scholar 

  37. Sultana N, Chilamkurti N, Peng W, Alhadad R (2019) Survey on sdn based network intrusion detection system using machine learning approaches. Peer-to-Peer Netw Appl 12(2):493–501

    Article  Google Scholar 

  38. Tao P, Sun Z, Sun Z (2018) An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6:13624–13631

    Article  Google Scholar 

  39. Verma A, Ranga V (2020) Machine learning based intrusion detection systems for iot applications. Wirel Pers Commun 111(4):2287–2310

    Article  Google Scholar 

  40. Wadkar M, Di Troia F, Stamp M (2020) Detecting malware evolution using support vector machines. Expert Syst Appl 143:113022

    Article  Google Scholar 

  41. Wang W, Du X, Shan D, Qin R, Wang N (2020) Cloud intrusion detection method based on stacked contractive Auto-Encoder and support vector machine. IEEE Trans Cloud Comput: 1–1

  42. Wang W, Wang X, Feng D, Liu J, Han Z, Zhang X (2014) Exploring permission-induced risk in android applications for malicious application detection. IEEE Trans Inf Foren Sec 9(11):1869–1882

    Article  Google Scholar 

  43. Wang W, Xi J, Chong A, Li L (2017) Driving style classification using a semisupervised support vector machine. IEEE Trans Hum Mach Sys 47 (5):650–660

    Article  Google Scholar 

  44. Wu H, Liu B, Su W, Zhang W, Sun J (2016) Deep filter banks for Land-Use scene classification. IEEE Geosci Remote Sens Lett 13(12):1895–1899

    Article  Google Scholar 

  45. Xu Q, Zhang C, Zhang L, Song Y (2016) The learning effect of different hidden layers stacked autoencoder. In: 2016 8th international conference on intelligent human-machine systems and cybernetics (IHMSC). IEEE, pp 148–151

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Acknowledgements

This paper is partially supported by the International Grants Number RDU192705 and UIC191516.

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Authors and Affiliations

  1. Faculty of Computing, University Malaysia Pahang, Pahang, Malaysia

    Md Arafatur Rahman & Ong Wei Wen

  2. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    A. Taufiq Asyhari & Yussuf Ahmed

  3. Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh

    Husnul Ajra

  4. Faculty Engineering, International Islamic University Malaysia, Selangor, Malaysia

    Farhat Anwar

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  1. Md Arafatur Rahman
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  2. A. Taufiq Asyhari
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  3. Ong Wei Wen
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Correspondence to Md Arafatur Rahman.

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Rahman, M.A., Asyhari, A.T., Wen, O.W. et al. Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection. Multimed Tools Appl 80, 31381–31399 (2021). https://doi.org/10.1007/s11042-021-10567-y

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  • DOI: https://doi.org/10.1007/s11042-021-10567-y

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