International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

A Review on Intrusion Detection Systems to Secure IoT Networks

Author NameAuthor Details

A. Arul Anitha, L. Arockiam

A. Arul Anitha[1]

L. Arockiam[2]

[1]Department of Computer Science, St. Joseph’s College (Autonomous) (Affiliated to Bharathidasan University), Tiruchirappalli, Tamil Nadu, India

[2]Department of Computer Science, St. Joseph’s College (Autonomous) (Affiliated to Bharathidasan University), Tiruchirappalli, Tamil Nadu, India

Cite this Article

DOI: 10.22247/ijcna/2022/211599

Pages : 38-50

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Abstract

The Internet of Things (IoT) and its rapid advancements will lead to everything being connected in the near future. The number of devices connected to the global network is increasing every day. IoT security challenges arise as a result of the large-scale incorporation of smart devices. Security issues on the Internet of Things have been the most focused area of research over the last decade. As IoT devices have less memory, processing capacity, and power consumption, the traditional security mechanisms are not suitable for IoT. A security mechanism called an Intrusion Detection System (IDS) has a crucial role in protecting the IoT nodes and networks. The lightweight nature of IoT nodes should be considered while designing IDS for the IoT. In this paper, the types of IDS, the major attacks on IoT, the recent research, and contributions to IDS in IoT networks are discussed, and an analytical survey is given based on the study. Though it is a promising area for research, IDS still needs further refinement to ensure high security for IoT networks and devices. Hence, further research, development, and lightweight mechanisms are required for IDS to provide a higher level of security to the resource-limited IoT network.

Index Terms

Attack

IoT

Intrusion

IDS

RPL

Security

Reference

  1. 1.
    A. Khraisat and A. Alazab, “A critical review of intrusion detection systems in the Internet of Things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges”, Cyber Security 4(18), 2021, DOI: 10.1186/s42400-021-00077-7
  2. 2.
    A. Colakovi and M. Hadziali, “Internet of Things (IoT): A Review of Enabling Technologies, Challenges, and Open Research Issues”, Computer Networks, 2018, DOI: 10.1016/j.comnet.2018.07.017.
  3. 3.
    E. C. Ugwuabonyi and E.Z. Orji, “Issues and Challenges in Security and Privacy of Internet of Things (IoT)”, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), 7(12), 2018, ISSN 2278-2540.
  4. 4.
    B. B. Zarpaelo, R.S. Miani, C.T. Kawakani and S. C. Alverenga, “A Survey of Intrusion Detection in Internet of Things”, Journal of Network and Computer Applications, 2017, DOI: 10.1016/j.jnca.2017.02.009.
  5. 5.
    A. Mayzaud, R. Badonnel and I. Chrisment, “A Taxonomy of Attacks in RPL-based Internet of Things”, International Journal of Network Security”, ACEEE a Division of Engineers Network, 18 (3), pp.459-473, 2016, DOI:10.6633/IJNS.201605.18(3), hal-01207859.
  6. 6.
    T. A. Tchakoucht and M. Ezziyyani, “Building A Fast Intrusion Detection System For High-Speed Networks: Probe and DoS Attacks Detection”, Procedia Computer Science, 127, pp. 521–530, 2018.
  7. 7.
    K.K. Patel and S.M. Patel, “Internet of Things-IoT: Definition, Characteristics, Architecture, Enabling Technologies, Application and Future Challenges”, International Journal of Engineering Science and Computing, 6(5), ISSN 2321- 3361, 2016, DOI: 10.4010/2016.1482.
  8. 8.
    V. Adat and B. B. Gupta, “Security in Internet of Things: issues, challenges, taxonomy, and architecture”, Telecommunication System, 2017, DOI: 10.1007/s11235-017-0345-9.
  9. 9.
    A. Tewari and B.B. Gupta, “Security, privacy and trust of different layers in Internet-of-Things (IoTs) framework”, Future Generation Computer Systems, 108, ISSN: 0167-739X, pp: 909-920, 2020, DOI: 10.1016/j.future.2018.04.027
  10. 10.
    R. Sahay, G. Geethakumari and K. Modugu, “Attack Graph based Vulnerability Assessment of Rank property in RPL-6LowPAN in IoT”, IEEE Explore, 2018, DOI: 10.1109/WF-IoT.2018.8355171
  11. 11.
    J. Deogirikar and A. Vidhate, “Security Attacks in IoT: A Survey. International Conference on IoT in Social, Mobile, Analytical and Cloud”, I-SMAC- 2017, IEEE, 2017.
  12. 12.
    A. R. Sfar, E. Natalizio, Y. Challal and Z. Chtourou, “A Roadmap for Security Challenges in the Internet of Things”, Digital Communications and Networks, 4, pp.118-137, 2018.
  13. 13.
    E E. Hemdan and D.H. Manjaiah, “Cybercrimes Investigation and Intrusion Detection in Internet of Things based on Data Science Methods”, Cognitive Computing for Big Data Systems over IoT, 2018, DOI: 10.1007/978-3-319-70688-7_2.
  14. 14.
    Y. Fu, C. Yan, J. Cao, O. Kore and X. Cao, “An Automata based Intrusion Detection method for Internet of Things”, Mobile Information Systems, Hindawi Publications, 2017(1750637), 2017, DOI: 10.1155/2017/1750637.
  15. 15.
    S. Raza, L. Wallgren and T. Voigt, “SVELTE: real-time intrusion detection in the Internet of Things”, Ad Hoc Network, 11(8), ISSN: 2661-2674, 2013, DOI:10.1016/j.adhoc.2013.04.014.
  16. 16.
    D. Shreenivas, S. Raza and T. Voigt, “Intrusion Detection in the RPL connected 6LoWPAN Networks”, Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security, IOTPTS’17, Abu Dhabi, United Arab Emirates, 2017.
  17. 17.
    B. Mbarek, M. Ge and T. Pitner, “Enhanced Network Intrusion Detection System Protocol for Internet of Things”, Proceedings of ACM SAC Conference (SAC’20), ACM, New York, Article 4, 2020, DOI: 10.1145/3341105.3373867.
  18. 18.
    P. P. Ioulianou, V. G. Vassilakis, I.D. Moscholios and M. D. Logothetis, “A Signature-based Intrusion Detection System for the Internet of Things”, International Conference on Information and Communication Technology Forum (ICTF-2018) ,Graz, Austria, 2018, https://www.researchgate.net/publication/ 326376629.
  19. 19.
    P. Wanda and H. J. Jie, “A survey of Intrusion Detection System”, International Journal of Informatics and Computation (IJICOM) 1(1), ISSN: 2685-8711, 2019.
  20. 20.
    H. Abdul-Ghani, D. Konstantas and M. Mahyoub, “A Comprehensive IoT Attacks Survey based on a Building-blocked Reference Model”, International Journal of Advanced Computer Science and Applications, Springer, 9(3), 2018.
  21. 21.
    Y. Lu and L.D. Xu, “Internet of Things (IoT) Cyber Security Research: A Review of Current Research Topics”, IEEE Internet of Things Journal, 2018, DOI: 10.1109/JIOT.2018.2869847.
  22. 22.
    C. Ramakrishna, G.K. Kumar, A.M. Reddy and P. Ravi, “A Survey on various IoT Attacks and its Countermeasures”, International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5(4), ISSN: 2394-2320, 2018.
  23. 23.
    H. Qu, L. Lei, X. Tang and W. Ping, “A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks”, Advances in Fuzzy Systems, Article ID: 4071851, 2018, DOI: 10.1155/2018/407185.
  24. 24.
    S. K. Biswas, “Intrusion Detection Using Machine Learning: A Comparison Study”, International Journal of pure and Applied Mathematics, 118 (19), pp.101-114, ISSN: 1311-8080 (print); ISSN: 1314-3395 (online), 2018.
  25. 25.
    N. Moustafa, B. Turnbull and K. R. Choo, “An Ensemble Intrusion Detection Technique based on proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things”, IEEE Internet of Things Journal, 2018, DOI:10.1109/JIOT.2018.2871719.
  26. 26.
    S. U. Jan, S. Ahmed, V. Shakov and I. Koo, “Toward a Lightweight Intrusion Detection System for the Internet of Things”, IEEE Access, 2019, DOI: 10.1109/ACCESS.2019.2907965.
  27. 27.
    M. Eskandari, Z. H. Janjua, M. Vecchio and F. Antonell, “Passban IDS: An Intelligent Anomaly Based Intrusion Detection System for IoT Edge Devices”, IEEE Internet of Things Journal, pp. (99):1-1, 2020, DOI: 10.1109/JIOT.2020.2970501.
  28. 28.
    O. Alkadi, N. Moustafa, B. Turnbull and K. R. Choo, “A Deep Blockchain Framework-enabled Collaborative Intrusion Detection for Protecting IoT and Cloud Networks”, IEEE Internet of Things Journal, 2020, DOI:10.1109/JIOT.2020.2996590.
  29. 29.
    M. A. Cheema, H. K. Qureshi, C. Chrysostomou and M. Lestas, “Utilizing Blockchain for Distributed Machine Learning based Intrusion Detection in Internet of Things”, 16th International Conference on Distributed Computing in Sensor Systems (DCOSS-2020), IEEE Xplore, 2020, DOI: 10.1109/DCOSS49796.2020.00074.
  30. 30.
    G. D. L. T. Parra, P. Rad, K. R. Choo and N. Beebe, “Detecting Internet of Things Attacks using Distributed Deep Learning”, Journal of Network and Computer Applications, 163(102662), ScienceDirect, 2020, DOI: 10.1016/j.jnca.2020.102662.
  31. 31.
    M. A. Alsoufi, S. Razak, M. M. Siraj, I. Nafea, F. A. Ghaleb, F. Saeed and M. Nasser, “Anomaly-based Intrusion Detection Systems in IoT using Deep Learning”, Applied Sciences, 11(18), 8383, 2021,DOI:10.3390/app11188383.
  32. 32.
    P. Kumar, G. P Gupta and R. Tripathi, “A distributed ensemble design based intrusion detection system using fog computing to protect the Internet of Things networks”, Journal of Ambient Intelligence and Humanized Computing, 12, pp. 9555–9572, 2020, DOI:10.1007/s12652-020-02696-3
  33. 33.
    L. Santos, R. Gonçalves, C. Rabadao and J. Martins, “A flow-based intrusion detection framework for internet of things networks”, Cluster Computing, Springer, 2021, DOI: 10.1007/s10586-021-03238-y
  34. 34.
    E. Benkhelifa, T. Welsh and W. Hamouda, “A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Towards Universal and Resilient Systems”, IEEE, 2018, DOI:10.1109/COMST.2018.2844742.
  35. 35.
    D. Oh, D. Kim and W. W. Ro, “A Malicious Pattern Detection Engine for Embedded Security Systems in the Internet of Things”, Sensors, 14 (12), ISSN: 24188–24211, 2014, DOI: 10.3390/s141224188.
  36. 36.
    T. H. Lee, T. H. Wen, L. H. Chang, H. S. Chiang and M.C. Hsieh, “A lightweight Intrusion Detection Scheme based on Energy Consumption Analysis in 6LowPAN”, Advanced Technologies, Embedded and Multimedia for Human-centric Computing, Lecture Notes in Electrical Engineering 260, Springer Netherlands, pp. 1205–1213, 2014.
  37. 37.
    A. Mehmood, M. Mukherjee, S. H. Ahmed, H. Song and M. M. Malik, “NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks”, Journal of Supercomputers, Springer Science+Business Media, LLC, Springer Nature, 2018, DOI:10.1007/s11227-018-2413-7
  38. 38.
    C. Cervantes, D. Poplade, M. Nogueira and A. Santos, “Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things”, IFIP/IEEE International Symposium on Integrated Network Management (IM), pp.606–611, 2015.
  39. 39.
    A. Sforzin and M. Conti, “RpiDS: Raspberry Pi IDS-A fruitful Intrusion Detection System for IoT”, International IEEE Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart World Congress, 2016, DOI:10.1109/UIC-ATC-Scalcom-CBDCom-IOP-SmartWorld.2016.114.
  40. 40.
    D. Midi, A. Rullo, A. Mudgerikar and E. Bertino, “KALIS: A system for knowledge-driven adaptable intrusion detection for the Internet of Things”, Proceedings of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS’17), 2017.
  41. 41.
    A. Wani and S. Revathi, “Analyzing Threats of IoT Networks Using SDN Based Intrusion Detection System (SDIoT-IDS)”, Smart and Innovative Trends in Next Generation Computing Technologies (NGCT-2017), Springer, CCIS 828, pp. 536–542, 2018.
  42. 42.
    J. Amaral, L. Oliveira, J. Rodrigues, G. Han and L. Shu, “Policy and Network-based Intrusion Detection System for IPv6-enabled Wireless Sensor Networks”, IEEE International Conference on Communications (ICC-2014), pp. 1796–1801, 2014.
  43. 43.
    N. K. Thanigaivelan, E. Nigussie, S. Virtanen and J. Isoaho, “Hybrid Internal Anomaly Detection System for IoT: Reactive Nodes with Cross-Layer Operation”, Security and Communication Networks, Article ID: 3672698, 2018, DOI: 10.1155/2018/3672698.
  44. 44.
    O. A. Okpe, O. A. John and S. Emmanuel, “Intrusion Detection in Internet of Things”, International Journal of Advanced Research in Computer Science, 9(1), ISSN: 0976-5697, 2018, DOI:10.26483/ijarcs.v9i1.5429.
  45. 45.
    V. Kumar, A. K. Das and D. Sinha, “UIDS: A Unified Intrusion Detection System for IoT Environment”, Evolutionary Intelligence, 14, pp. 47–59, 2021, DOI: 10.1007/s12065-019-00291-w
  46. 46.
    L. Santos, C. Rabadão and R. Gonçalves, “Intrusion Detection Systems in Internet of Things: A Literature Review”, ResearchGate, 2018, DOI: 10.23919/CISTI.2018.8399291.
  47. 47.
    I. Ulla and Q. H. Mahmoud, “Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks”, IEEE Access, 9, e-ISSN: 2169-3536, pp. 103906–103926, 2021, DOI: 1109/ACCESS.2021.309402.
  48. 48.
    R. Mitchell and I. Chen, “A Survey of Intrusion Detection Techniques for Cyber-physical Systems”, ACM Computing Surveys (CSUR), 46 (4), 55, 2014.
  49. 49.
    P. V. Astillo, J. Jeong, W. C. Chien, B. Kim, J. S. Jang, I. You, “SMDAps: A Specification-based Misbehavior Detection System for Implantable Devices in Artificial Pancreas System”, Journal of Internet Technology, 22(1), e-ISSN:2079-4029, 2021, DOI: 10.3966/160792642021012201001
  50. 50.
    H. Bostani and M. Sheikhan, “Hybrid of Anomaly-Based and Specification-Based IDS for Internet of Things Using Unsupervised OPF Based on MapReduce Approach”, Computer Communications, 98(15), pp. 52-71, 2017, DOI:10.1016/j.comcom.2016.12.001.
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