Skip to main content
SpringerLink
Log in

A Survey of Machine Learning Techniques for IoT Security

  • Conference paper
  • First Online:
Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1500))

Included in the following conference series:

Abstract

The Internet of Things (IoT) has begun to reform and alter our lives due to its rapid expansion. The internet-connected deployment of a significant number of things has opened the vision of the smart world around us, opening the way for automation and massive data creation and collecting. Because of the automation and constant influx of personal and professional data into the digital world, attackers have a fertile field on which to launch various cyber-attacks, making IoT security a major issue. As a result, early detection and prevention of such risks are essential for avoiding catastrophic repercussions. The research gives a brief overview of the technology, with a focus on different assaults and anomalies, as well as their detection using an adaptive intrusion detection system (IDS). The in-depth examination and evaluation of several machine learning and deep learning-based network intrusion detection systems are presented in this paper. Furthermore, the study highlights a number of research issues in order to enable additional improvements in ways to deal with unique difficulties.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutorials 17(4), 2347–2376 (2015)

    Article  Google Scholar 

  2. Tran, T.K., Phan, T.T., Thien Khai and Tuoi Thi: Capturing contextual factors in sentiment classification: an ensemble approach. IEEE Access 8, 116856–116865 (2020)

    Article  Google Scholar 

  3. Tran, T.K., Phan, T.T.: Deep learning application to ensemble learning-the simple, but effectiveApproach sentiment classifying. Appl. Sci. 9(13), 2760 (2019)

    Article  Google Scholar 

  4. Florez, D.A.: International Case Studies of Smart Cities: Medellin, Colombia. Inter-American Development Bank, June 2016

    Google Scholar 

  5. Bakhsh, S.T., Alghamdi, S., Alsemmeari, R.A., Hassan, S.R.: An adaptive intrusion detection and prevention system for Internet of Things. Int. J. Distributed Sensor Networks 15(11) (2019)

    Google Scholar 

  6. Wahab, O.A., Mourad, A., Otrok, H., Taleb, T.: Federated machine learning: survey, multi-level classification, desirable criteria and future directions in communication and networking systems. IEEE Commun. Surv. Tutorials 23(2), 1342–1397 (2021)

    Article  Google Scholar 

  7. Rana, B., Singh, Y., Singh, P.K.: A systematic survey on internet of things: energy efficiency and interoperability perspective. Trans. Emerging Telecommun. Technol. 32(8), e4166 (2021)

    Google Scholar 

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

    Article  Google Scholar 

  9. Iot security: Risks, examples, and solutions. https://www.emnify.com/en/resources/iot-security. Accessed 26 July 2021

  10. Macedo, E.L.C., et al.: On the security aspects of Internet of Things: A systematic literature review (2019)

    Google Scholar 

  11. Hameed, S., Khan, F.I., Hameed, B.: Understanding Security Requirements and Challenges in Internet of Things (IoT): A Review (2019)

    Google Scholar 

  12. Obaidat, M.A., Obeidat, S., Holst, J., Al Hayajneh, A., Brown, J.: A comprehensive and systematic survey on the internet of things: security and privacy challenges, security frameworks, enabling technologies, threats, vulnerabilities and countermeasures. Computers 9(2), 44 (2020)

    Google Scholar 

  13. Abdul-Ghani, H.A., Konstantas, D., Mahyoub, M.: A comprehensive IoT attacks survey based on a building-blocked reference model. Int. J. Adv. Comput. Sci. Appl. 9(3), 355–373 (2018)

    Google Scholar 

  14. Into the battlefield: A security guide to iot botnets. http://www.trendmicro.com/vinfo. Accessed 26 July 2021

  15. Meng, W.: Intrusion detection in the era of IoT: building trust via traffic filtering and sampling. Computer 51(7), 36–43 (2018)

    Article  Google Scholar 

  16. Nawir, M., Amir, A., Yaakob, N., Lynn, O.B.: Internet of Things (IoT): taxonomy of security attacks. In: 2016 3rd International Conference on Electronic Design, ICED 2016 (2017)

    Google Scholar 

  17. Butun, I., Osterberg, P., Song, H.: Security of the internet of things: vulnerabilities, attacks, and countermeasures. IEEE Commun. Surv. Tutorials 22(1), 616–644 (2020)

    Article  Google Scholar 

  18. Atlam, H.F., Wills, G.B.: IoT Security, Privacy. Safety and Ethics, In Internet of Things (2020)

    Google Scholar 

  19. Divyatmika, Sreekesh, M.: A two-tier network based intrusion detection system architecture using machine learning approach. In: International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016 (2016)

    Google Scholar 

  20. Anthi, E., Williams, L., Burnap, P.: Pulse: an adaptive intrusion detection for the internet of things. In: IET Conference Publications, vol. 2018 (2018)

    Google Scholar 

  21. Rathore, S., Park, J.H.: Semi-supervised learning based distributed attack detection framework for IoT. Appl. Soft Comput. J. 72, 79–89 (2018)

    Article  Google Scholar 

  22. Ozay, M., Esnaola, I., Vural, F.T.Y., Kulkarni, S.R., Vincent Poor, H.: Machine learning methods for attack detection in the smart grid. IEEE Trans. Neural Networks Learn. Syst. 27(8), 1773–1786 (2016)

    Google Scholar 

  23. Khan, N., Abdullah, J., Khan, A.S.: Defending malicious script attacks using machine learning classifiers. Wireless Communications and Mobile Computing (2017)

    Google Scholar 

  24. Abdulraheem, M.H., Ibraheem, N.B., Mohammed Hamid and Najla Badie: A detailed analysis of new intrusion detection dataset. J. Theoretical Appl. Inf. Technol. 97(17), 4519–4537 (2019)

    Google Scholar 

  25. Thanh, C.T.: A novel approach for intrusion detection based on deep belief network. In: Advances in Intelligent Systems and Computing, vol. 1225 AISC (2020)

    Google Scholar 

  26. Ddos evaluation dataset (cic-ddos2019). https://www.unb.ca/cic/datasets/ddos-2019.html. Accessed 10 July 2021

  27. Intrusion detection evaluation dataset (cic-ids2017). https://www.unb.ca/cic/datasets/ids-2017.html. Accessed 10 July 2021

  28. Intrusion detection evaluation dataset (iscxids2012). https://www.unb.ca/cic/datasets/ids.html. Accessed 10 July 2021

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

    Article  Google Scholar 

  30. Isot botnet dataset. https://www.uvic.ca/engineering/ece/isot/datasets/index.php. Accessed 20 June 2021

  31. Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference, MilCIS 2015 - Proceedings (2015)

    Google Scholar 

  32. Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Futur. Gener. Comput. Syst. 100, 779–796 (2019)

    Article  Google Scholar 

  33. Rahul, V.K., Vinayakumar, R., Soman, Kp, Poornachandran, P.: Evaluating shallow and deep neural networks for network intrusion detection systems in cyber security. In: 2018 9th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2018 (2018)

    Google Scholar 

  34. Roopak, M., Tian, G.Y., Chambers, J.: Deep learning models for cyber security in IoT networks. In: IEEE 9th Annual Computing and Communication Workshop and Conference. CCW, 2019 (2019)

    Google Scholar 

  35. Samaila, M.G., Neto, M., Fernandes, D.A.B., Freire, M.M., Inácio, P.R.M.: Challenges of securing Internet of Things devices: a survey. Secur. Privacy 1(2), e20 (2018)

    Google Scholar 

  36. Neshenko, N., Bou-Harb, E., Crichigno, J., Kaddoum, G., Ghani, N.: Demystifying IoT security: an exhaustive survey on IoT vulnerabilities and a first empirical look on internet-scale IoT exploitations. IEEE Commun. Surv. Tutorials 21(3), 2702–2733 (2019)

    Article  Google Scholar 

  37. Hasan, M., Islam, M.M., Zarif, M.I.I., Hashem, M.M.A.: Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things 7, 100059 (2019)

    Google Scholar 

  38. Anand, P., Singh, Y., Selwal, A., Alazab, M., Tanwar, S., Kumar, N.: IoT vulnerability assessment for sustainable computing: threats, current solutions, and open challenges. IEEE Access 8, 168825–168853 (2020)

    Article  Google Scholar 

  39. Yazdinejad, A., Parizi, R.M., Dehghantanha, A., Zhang, Q., Choo, K.K.R.: An energy-efficient SDN controller architecture for IoT networks with blockchain-based security. IEEE Trans. Serv. Comput. 13(4), 625–638 (2020)

    Article  Google Scholar 

  40. Rachit, S.B., Ragiri, P.R.: Security trends in Internet of Things: a survey (2021)

    Google Scholar 

  41. Noor, U., Anwar, Z., Amjad, T., Choo, K.K.R.: A machine learning-based FinTech cyber threat attribution framework using high-level indicators of compromise. Future Gener. Comput. Syst. 96, 227–242 (2019)

    Article  Google Scholar 

  42. Oyelade, J., et al.: Data clustering: algorithms and its applications. In: Proceedings - 2019 19th International Conference on Computational Science and Its Applications, ICCSA 2019 (2019)

    Google Scholar 

  43. Ravi, N., Shalinie, S.M.: Semisupervised-learning-based security to detect and mitigate intrusions in IoT network. IEEE Internet Things J. 7(11), 11041–11052 (2020)

    Article  Google Scholar 

  44. Lee, S.Y., Wi, S.R., Seo, E., Jung, J.K., Chung, Y.M.: ProFiOt: Abnormal Behavior Profiling (ABP) of IoT devices based on a machine learning approach. In: 2017 27th International Telecommunication Networks and Applications Conference, ITNAC 2017, vol. 2017, January 2017

    Google Scholar 

  45. Yang, K., Ren, J., Zhu, Y., Zhang, W.: Active learning for wireless IoT intrusion detection. IEEE Wireless Commun. 25(6), 19–25 (2018)

    Article  Google Scholar 

  46. Shafi, Q., Basit, A., Qaisar, S., Koay, A., Welch, I.: Fog-assisted SDN controlled framework for enduring anomaly detection in an IoT network. IEEE Access 6, 73713–73723 (2018)

    Article  Google Scholar 

  47. Hussain, F., Hussain, R., Hassan, S.I., Hossain, E.: Machine learning in IoT security: current solutions and future challenges. IEEE Commun. Surv. Tutorials 22(3), 1686–1721 (2020)

    Article  Google Scholar 

  48. Chauhan, J., Seneviratne, S., Yining, H., Misra, A., Seneviratne, A., Lee, Y.: Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks. Computer 51(5), 60–67 (2018)

    Article  Google Scholar 

  49. Xiao, L., Li, Y., Han, G., Liu, G., Zhuang, W.: PHY-layer spoofing detection with reinforcement learning in wireless networks. IEEE Trans. Vehicular Technol. 65(12), 10037–10047 (2016)

    Article  Google Scholar 

  50. Shi, C., Liu, J., Liu, H., Chen, Y.: Smart User authentication through actuation of daily activities leveraging wifi-enabled IoT. In: Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), volume Part F129153 (2017)

    Google Scholar 

  51. Abebe Abeshu Diro and Naveen Chilamkurti: Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Comput. Syst. 82, 761–768 (2018)

    Article  Google Scholar 

  52. Abeshu, A., Chilamkurti, N.: Deep learning: the frontier for distributed attack detection in fog-to-things computing. IEEE Commun. Magaz. 56(2), 169–175 (2018)

    Article  Google Scholar 

  53. Doshi, R., Apthorpe, N., Feamster, N., Machine learning DDoS detection for consumer internet of things devices. In: Proceedings of IEEE Symposium on Security and Privacy Workshops. SPW 2018 (2018)

    Google Scholar 

  54. Tan, Z., Jamdagni, A., He, X., Nanda, P., Liu, R.P.: A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Trans. Parallel Distrib. Syst. 25(2), 447–456 (2014)

    Google Scholar 

  55. Shukla, P.: ML-IDS: a machine learning approach to detect wormhole attacks in Internet of Things. In: 2017 Intelligent Systems Conference, IntelliSys 2017, vol. 2018, January 2018

    Google Scholar 

  56. Canedo, J., Skjellum, A.: Using machine learning to secure IoT systems. In: 2016 14th Annual Conference on Privacy, p. 2016. PST, Security and Trust (2016)

    Google Scholar 

  57. Nesa, N., Ghosh, T., Banerjee, I.: Non-parametric sequence-based learning approach for outlier detection in IoT. Future Gener. Comput. Syst. 82, 412–421 (2018)

    Article  Google Scholar 

  58. Viegas, E., Santin, A., Oliveira, L., França, A., Jasinski, R., Pedroni, V.: A reliable and energy-efficient classifier combination scheme for intrusion detection in embedded systems. Comput. Secur. 78, 16–32 (2018)

    Article  Google Scholar 

  59. Pajouh, H.H., Javidan, R., Khayami, R., Dehghantanha, A., Choo, K.K.R.: A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerging Top. Comput. 7(2), 314–323 (2019)

    Article  Google Scholar 

  60. Azmoodeh, A., Dehghantanha, A., Choo, K.K.R.: Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4(1), 88–95 (2019)

    Article  Google Scholar 

  61. Zhou, W., Bin, Yu.: A cloud-Assisted malware detection and suppression framework for wireless multimedia system in IOT based on dynamic differential game. China Commun. 15(2), 209–223 (2018)

    Article  Google Scholar 

  62. Su, J., Vasconcellos, V.D., Prasad, S., Daniele, S., Feng, Y., Sakurai, K.: Lightweight classification of IoT malware based on image recognition. In: Proceedings - International Computer Software and Applications Conference, vol. 2 (2018)

    Google Scholar 

  63. Karbab, E.M.B., Debbabi, M., Derhab, A., Mouheb, D.: MalDozer: Automatic framework for android malware detection using deep learning. In: DFRWS 2018 EU - Proceedings of the 5th Annual DFRWS Europe (2018)

    Google Scholar 

  64. Junfei Qiu, Qihui Wu, Guoru Ding, Yuhua Xu, and Shuo Feng. A survey of machine learning for big data processing, 2016

    Google Scholar 

  65. Samuel Amalorpava Mary Rajee and Arulraj Merline. Machine intelligence technique for blockage effects in next-generation heterogeneous networks. Radioengineering, 29(3), 2020

    Google Scholar 

  66. Mustafizur R Shahid, Gregory Blanc, Zonghua Zhang, and Hervé Debar. Machine Learning for IoT Network Monitoring. RESSI (Rendez-Vous de la Recherche et de l’Enseignement de la Sécurité des Systèmes d’Information), 2019

    Google Scholar 

  67. Srinivasan, S.M., Truong-Huu, T., Gurusamy, M.: Machine learning-based link fault identification and localization in complex networks. IEEE Internet Things J. 6(4), 6556–6566 (2019)

    Google Scholar 

  68. Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., Alazab, A.: A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks. Electronics (Switzerland) 8(11), 1210 (2019)

    Google Scholar 

  69. Prabavathy, S., Sundarakantham, K., Shalinie, S.M.L Design of cognitive fog computing for intrusion detection in Internet of Things. J. Commun. Networks 20(3), 291–298 (2018)

    Google Scholar 

  70. Moustafa, N., Turnbull, B., Choo, K.K.R.: An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. IEEE Internet Things J. 6(3), 4815–4830 (2019)

    Article  Google Scholar 

  71. Thanh, C.T.: Modeling and Testing power consumption rate of low-power wi-fi sensor motes for smart building applications. In: Dang, T.K., Küng, J., Wagner, R., Thoai, N., Takizawa, M. (eds.) FDSE 2018. LNCS, vol. 11251, pp. 449–459. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03192-3_34

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, University of Foreign Languages and Information Technology, Ho Chi Minh City, Vietnam

    Cao Tien Thanh

Authors
  1. Cao Tien Thanh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cao Tien Thanh .

Editor information

Editors and Affiliations

  1. HCMC University of Technology (HCMUT), Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thanh, C.T. (2021). A Survey of Machine Learning Techniques for IoT Security. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8062-5_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8061-8

  • Online ISBN: 978-981-16-8062-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation