Skip to main content
SpringerLink
Log in

A Comprehensive Review of IoT-Based IDS Using Intelligence Technique

  • Conference paper
  • First Online:
Advances in Data and Information Sciences

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 522))

  • 285 Accesses

Abstract

The Internet of Things (IoT) is a collection of connected computing devices that includes several of our everyday gadgets which allow data to be transferred over the network. The IoT system has its application in various fields including, transportation, smart home, hospitals, smart grid, etc. The ability of devices connected to the web makes them exposed to multiple security intrusions and affects the security traits of the system. Hence, it is vital to investigate intrusion techniques in the IoT context to prevent or identify these intrusions. The primary focus of this review is on intrusion detection systems (IDS) for the IoT system. Therefore, this paper presents a comprehensive review of the latest IDS schemes for the IoT system designed using intelligence techniques, including machine learning, deep learning, and bio-inspired learning. The issues and challenges faced by the IoT-based IDS are presented. Finally, the comparative study and discussion on reviewed IDS scheme are described.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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

Similar content being viewed by others

References

  1. Sha K, Yang TA, Wei W, Davari S (2020) A survey of edge computing-based designs for IoT security. Digital Commun Netw 6(2):195–202

    Article  Google Scholar 

  2. Tein WC, Chen WS, Ban T, Kuo SY (2020) Machine learning framework to analyze IoT malware using ELF and opcode features. Digital Threats Res Pract 1(1):1–19

    Article  Google Scholar 

  3. Thakkar A, Lohiya R (2020) A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges. Arch Comput Methods Eng 1–33

    Google Scholar 

  4. Geurts P, Irrthum A, Wehenkel L (2009) Supervised learning with decision tree-based methods in computational and systems biology. Mol BioSyst 5(12):1593–1605

    Article  Google Scholar 

  5. Ferrag MA, Maglaras L, Ahmin A, Derdour M, Janicke H (2020) RDTIDS: rules and decision tree-based intrusion detection system for internet-of-things networks. Future Internet 12(3):44

    Article  Google Scholar 

  6. Anthi E, Williams L, Słowinska M, Theodorakopoulos G, Burnap P (2019) A supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J 6(5):9042–9053

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Jha J, Ragha L (2013) Intrusion detection system using support vector machine. Int J Appl Inf Syst (IJAIS) 3:25–30

    Google Scholar 

  9. Al-Garadi MA, Mohamed A, Al-Ali AK, Du X, Ali I, Guizani M (2020) A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun Surv Tutorials 22(3):1646–1685

    Article  Google Scholar 

  10. 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

    Google Scholar 

  11. Liu Y, Wang Y, Zhang J (2012) New machine learning algorithm: random forest. In: International conference on information computing and applications

    Google Scholar 

  12. Kumar P, Gupta GP, Tripathi R (2020) A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks. J Ambient Intell Humanized Comput 1–18

    Google Scholar 

  13. Shanthamallu US, Spanias A, Tepedelenlioglu C, Stanley M (2017) A brief survey of machine learning methods and their sensor and IoT applications. In: 2017 8th international conference on information, intelligence, systems & applications (IISA), Larnaca, Cyprus

    Google Scholar 

  14. de Souza CA, Westphall CB, Machado RB, Sobral JBM, Vieira GDS (2020) Hybrid approach to intrusion detection in fog-based IoT environments. Comput Netw 180:107417

    Article  Google Scholar 

  15. Fadlullah ZM, Tang F, Mao B, Kato N, Akashi O, Inoue T, Mizutani K (2017) State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun Surv Tutorials 19(4):2432–2455

    Article  Google Scholar 

  16. Vu L, Nguyen QU, Nguyen DN, Hoang DT, Dutkiewicz E (2020) Deep transfer learning for IoT attack detection. IEEE Access 8:107335–107344

    Article  Google Scholar 

  17. Rezvy S, Luo Y, Petridis M, Lasebae M, Zebin T (2019) An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks. In: 2019 53rd annual conference on information sciences and systems (CISS), Baltimore, MD, USA

    Google Scholar 

  18. Roopak M, Tian GY, Chambers J (2019) Deep learning models for cyber security in IoT networks. In: 2019 IEEE 9th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA

    Google Scholar 

  19. Smys S, Basar A, Wang H (2020) Hybrid intrusion detection system for internet of things (IoT). J ISMAC 2(4):190–199

    Article  Google Scholar 

  20. Zhang Q, Yang LT, Chen Z, Li P (2018) A survey on deep learning for big data. Inform Fusion 42:146–157

    Article  Google Scholar 

  21. Aldweesh A, Derhab A, Emam AZ (2020) Deep learning approaches for anomaly-based intrusion detection systems: a survey, taxonomy, and open issues. Knowl-Based Syst 189:105124

    Article  Google Scholar 

  22. Popoola SI, Adebisi B, Hammoudeh M, Gacanin H, Gui G (2021) Stacked recurrent neural network for botnet detection in smart homes. Comput Electr Eng 92(4):107039

    Article  Google Scholar 

  23. Popoola SI, Adebisi B, Ande R, Hammoudeh M, Anoh K, Atayero AA (2021) SMOTE-DRNN: a deep learning algorithm for botnet detection in the internet-of-things networks. Sensors 21(9):2985

    Article  Google Scholar 

  24. Davahli A, Shamsi M, Abaei G (2020) Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks. J Ambient Intell Humaniz Comput 11(11):5581–5609

    Google Scholar 

  25. Bansal JC, Sharma H, Jadon SS, Clerc M (2014) Spider monkey optimization algorithm for numerical optimization. Memetic Comput 6(1):31–47

    Article  Google Scholar 

  26. Otoum Y, Liu D, Nayak A (2022) DL‐IDS: a deep learning–based intrusion detection framework for securing IoT. Trans Emerg Telecommun Technol 33(3):e3803

    Google Scholar 

  27. Parimala G, Kayalvizhi R (2021) An effective intrusion detection system for securing IoT using feature selection and deep learning. In: 2021 international conference on computer communication and informatics (ICCCI)

    Google Scholar 

  28. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. In: Advances in engineering software, pp 46–61

    Google Scholar 

  29. Shorman AA, Faris H, Aljarah I (2020) Unsupervised intelligent system based on one class support vector machine and grey wolf optimization for IoT botnet detection. J Ambient Intell Humaniz Comput 11(7):2809–2825

    Article  Google Scholar 

  30. Krishna ESP, Arunkumar T (2021) Hybrid particle swarm and gray wolf optimization algorithm for IoT intrusion detection system. Int J Intell Eng Syst 14(4):66–76

    Google Scholar 

  31. Kennedy J (2006) Handbook of nature-inspired and innovative computing. In: Swarm intelligence. Springer, Berlin, pp 187–219

    Google Scholar 

  32. Keserwani PK, Govil MC, Pilli ES, Govil P (2021) A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model. J Reliable Intell Environ 7(1):3–21

    Google Scholar 

  33. Liu J, Yang D, Lian M, Li M (2021) Research on intrusion detection based on particle swarm optimization in IoT. IEEE Access 9:38254–38268

    Article  Google Scholar 

  34. Sicato JCS, Singh SK, Rathore S, Park JH (2020) A comprehensive analyses of intrusion detection system for IoT environment. J Inf Process Syst 16(4):975–990

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University School of Information Communication and Technology, Guru Gobind Singh Indraprastha University, Delhi, India

    Richa Singh & R. L. Ujjwal

Authors
  1. Richa Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. L. Ujjwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Richa Singh .

Editor information

Editors and Affiliations

  1. Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  2. Associate Professor, CSED, National Institute of Technology Agartala, Tripura, India

    Munesh C. Trivedi

  3. Faculty of Engineering and Science, University of Agder, Agder, Norway

    Mohan L. Kolhe

  4. Dept. of Computer Sci & Engineering, R. B. S. Engineering Technical Campus, Agra, Uttar Pradesh, India

    Brajesh Kumar Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, R., Ujjwal, R.L. (2023). A Comprehensive Review of IoT-Based IDS Using Intelligence Technique. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_11

Download citation

Publish with us

Policies and ethics

Search

Navigation