Intrusion detection system and mitigation of threats in IoT networks using AI techniques: A review

Main Article Content

Geo Francis E
S. Sheeja

Abstract

In recent times, IoT has been used in a wide range of applications for improving the quality of life. Conversely, IoT turns out to be progressively a superlative target for malicious attacks due to its huge range of openness, distributed nature, and objects. However, for maintaining IoT system security, there is a need for an effective Intrusion detection system (IDS), standing as a fundamental tool in the cyber security environment, which implements a detector that uninterruptedly observes the network traffic. Therefore, the network requires an efficient IDS system for detecting various attacks. Various IDS systems have been implemented for detecting intrusion in the IoT network; however, it is required to have a review of recent developments. The present study, therefore, reviews a range of existing IDS models that are employed in IoT networks for detecting intrusion along with recent threats. Various datasets employed in IDS and the challenges faced by IDS are also explored in this study. This study is implemented with a futuristic vision to improve the existing IDSs competent enough to face the latest attacks and threats in IoT Networks.

Article Details

How to Cite
Francis E, G. . ., & S. Sheeja. (2023). Intrusion detection system and mitigation of threats in IoT networks using AI techniques: A review. Engineering and Applied Science Research, 50(6), 633–645. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/250974
Section
REVIEW ARTICLES

References

Fatani A, Abd Elaziz M, Dahou A, Al-Qaness MAA, Lu S. IoT intrusion detection system using deep learning and enhanced transient search optimization. IEEE Access. 2021;9:123448-64.

Wang Y, Ma J, Sharma A, Singh PK, Gaba GS, Masud M, et al. An exhaustive research on the application of intrusion detection technology in computer network security in sensor networks. J Sens. 2021;2021:1-11.

Lombardi M, Pascale F, Santaniello D. Internet of Things: a general overview between architectures, protocols and applications. Information. 2021;12(2):87.

Ali SR. IoT: the revolutionary tech and its challenges in the modern technological landscape [thesis]. United States: Governors State University; 2022.

Firouzi F, Farahani B, Weinberger M, DePace G, Aliee FS. Iot fundamentals: definitions, architectures, challenges, and promises. In: Firouzi F, Chakrabarty K, Nassif S, editors. Intelligent Internet of Things. Cham: Springer; 2020. p. 3-50.

Krishnamurthi R, Kumar A, Gopinathan D, Nayyar A, Qureshi B. An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors. 2020;20(21):6076.

Skouteli E. Cybersecurity and the Internet of Things [thesis]. Greece: The International Hellenic University (IHU); 2023.

Rastogi A. A study of the architectures, protocols, and applications of the Internet of Things (IoT). Int J Food Nutr Sci. 2022;11(6):1016-23.

Thakral M, Singh RR, Kalghatgi BV. Cybersecurity and ethics for IoT system: a massive analysis. In: Saxena S, Pradhan AK, editors. Internet of Things. Transactions on Computer Systems and Networks. Singapore: Springer; 2022. p. 209-33.

Tran-Dang H, Kim DS. Fog computing: fundamental concepts and recent advances in architectures and technologies. Cooperative and Distributed Intelligent Computation in Fog Computing. Cham: Spromger; 2023. p. 1-18.

Gaurav A, Gupta BB, Hsu CH, Yamaguchi S, Chui KT. Fog layer-based DDoS attack detection approach for internet-of-things (IoTs) devices. IEEE International Conference on Consumer Electronics (ICCE); 2021 Jan 10-12; Las Vegas, USA. USA: IEEE; 2021. p. 1-5.

Labiod Y, Amara Korba A, Ghoualmi N. Fog computing-based intrusion detection architecture to protect iot networks. Wireless Pers Commun. 2022;125(1):231-59.

HaddadPajouh H, Khayami R, Dehghantanha A, Choo KKR, Parizi RM. AI4SAFE-IoT: an AI-powered secure architecture for edge layer of Internet of Things. Neural Comput Applic. 2020;32:16119-33.

Khan MA, Khan MA, Jan SU, Ahmad J, Jamal SS, Shah AA, et al. A deep learning-based intrusion detection system for MQTT enabled IoT. Sensors. 2021;21(21):7016.

Gassais R, Ezzati-Jivan N, Fernandez JM, Aloise D, Dagenais MR. Multi-level host-based intrusion detection system for Internet of Things. J Cloud Comp. 2020;9:1-16.

Lin F, Zhou Y, An X, You I, Choo KKR. Fair resource allocation in an intrusion-detection system for edge computing: ensuring the security of Internet of Things devices. IEEE Consum Electron Mag. 2018;7(6):45-50.

Mohamed RH, Mosa FA, Sadek RA. Efficient intrusion detection system for IoT environment. Int J Adv Comput Sci Appl. 2022;13(4):572-578.

Zhong M, Zhou Y, Chen G. Sequential model based intrusion detection system for IoT servers using deep learning methods. Sensors. 2021;21(4):1113.

Mothukuri V, Khare P, Parizi RM, Pouriyeh S, Dehghantanha A, Srivastava G. Federated-Learning-Based anomaly detection for IoT security attacks. IEEE Internet Things J. 2022;9(4):2545-54.

Idrissi I, Boukabous M, Azizi M, Moussaoui O, El Fadili H. Toward a deep learning-based intrusion detection system for IoT against botnet attacks. IAES Int J Artif Intell. 2021;10(1):110-20.

Basati A, Faghih MM. APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder. Neural Comput Applic. 2023:35:4813-33.

Mosaiyebzadeh F, Rodriguez LGA, Batista DM, Hirata R. A network intrusion detection system using deep learning against mqtt attacks in IoT. IEEE Latin-American Conference on Communications (LATINCOM); 2021 Nov 17-19; Santo Domingo, Dominican Republic. USa: IEEE; 2021. p. 1-6.

Jothi B, Pushpalatha M. WILS-TRS—a novel optimized deep learning based intrusion detection framework for IoT networks. Pers Ubiquit Comput. 2023;27:1285-301.

Jithu P, Shareena J, Ramdas A, Haripriya AP. Intrusion detection system for iot botnet attacks using deep learning. SN Comput Sc. 2021;2(3):1-8.

Liu C, Antypenko R, Sushko I, Zakharchenko O. Intrusion detection system after data augmentation schemes based on the VAE and CVAE. IEEE Trans Reliab. 2022;71(2):1000-10.

Ravi V, Chaganti R, Alazab M. Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system. Comput Electr Eng. 2022;102:108156.

Telikani A, Shen J, Yang J, Wang P. Industrial IoT intrusion detection via evolutionary cost-sensitive learning and fog computing. IEEE Internet Things J. 2022;9(22):23260-71.

Yang X, Peng G, Zhang D, Lv Y. An enhanced intrusion detection system for IoT networks based on deep learning and knowledge graph. Secur Commun Netw. 2022;2022:1-21.

Ahmad UB, Akram MA, Mian AN. Low-latency intrusion detection using a deep neural network. IT Prof. 2022;24(3):67-72.

Boopathi M. Henry MaxNet: tversky index based feature selection and competitive swarm henry gas solubility optimization integrated Deep Maxout network for intrusion detection in IoT. Int J Intell Robot Appl. 2022;6(2):365-83.

Campos EM, Saura PF, González-Vidal A, Hernández-Ramos JL, Bernabé JB, Baldini G, et al. Evaluating federated learning for intrusion detection in Internet of Things: review and challenges. Comput Netw. 2022;203:108661.

Heidari A, Jabraeil Jamali MA. Internet of Things intrusion detection systems: a comprehensive review and future directions. Cluster Comput. 2023;26:3753-80.

Atlam HF, Wills GB. IoT security, privacy, safety and ethics. In: Farsi M, Daneshkhah A, Hosseinian-Far A, Jahankhani H, editors. Digital twin technologies and smart cities. Cham: Springer; 2020. p. 123-49.

Chang CH, Guajardo J, Holcomb D, Regazzoni F, Rührmair U. ASHES 2018-Workshop on Attacks and Solutions in Hardware Security. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security; 2018 Oct 15-19; Toronto, Canada. New York: ACM; 2018. p. 2168-70.

Micro.ai. 10 Types of cyber security attacks in IoT [Internet]. 2019 [cited 2019 Nov 12]. Available from: https://micro.ai/blog/10-types-of-cyber-security-attacks-in-the-iot.

Zara S. Real-World examples of cyber attacks and their impact [Internet]. 2023 [cited 2023 Jul 25]. Available from: https://kahedu.edu.in/real-world-examples-of-cyber-attacks-and-their-impact/#:~:text=Yahoo%20was%20hacked%20online%3 A%20One,but%20bank%20information%20was%20not.

MicroAI. Cyber-Security-Top Threats for 2022 [Internet]. 2022 [cited 2022 Jun 17]. Available from: https://micro.ai/blog/cyber-security-top-threats-for-2022.

Lincoln Laboratory, MIT. DARPA intrusion detection evaluation dataset [Internet]. 1998 [cited 2022 Jun 17]. Available from: https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset.

The UCI KDD Archive, Information and Computer Science University of California. KDD Cup 1999 Data [Internet]. 1999 [cited 2022 Jun 17]. Available from: https://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

The UCI KDD Archive, Information and Computer Science University of California. Intrusion detector learning [Internet]. 1999 [cited 2022 Jun 17]. Available from: https://kdd.ics.uci.edu/databases/kddcup99/task.html.

Tavallaee M, Bagheri E, Lu W, Ghorbani AA. A detailed analysis of the KDD CUP 99 data set. IEEE symposium on computational intelligence for security and defense applications; 2009 Jul 8-10; Ottawa, Canada. USA: IEEE; 2009. p. 1-6.

Sekhar C, Rao KV, Prasad MK. Classification of the DDoS Attack over flash crowd with DNN using world cup 1998 and CAIDA 2007 Datasets. i-Manager's J Softw Eng. 2021;15(3):29-36.

Dwivedi S, Vardhan M, Tripathi S, Shukla AK. Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection. Evol Intel. 2020;13(1):103-17.

ALDAPA group. gureKddcup and gureKddcup6percent dataset. Spain: Computer Architecture And Technology Department, University of basque Country; 2019.

Sequeira D. Intrusion prevention systems-security's silver bullet? [Internet]. 2002 [cited 2016 Sep 9]. Available from: https://www.sans.org/white-papers/366/?show366.php&cat=detection.

Manhas J, Kotwal S. Implementation of intrusion detection system for Internet of Things using machine learning techniques. In: Giri KJ, Parah SA, Bashir R, Muhammad K, editors. Multimedia Security. Algorithms for Intelligent Systems. Singapore: Springer; 2021. p. 217-37.

Smys S, Basar A, Wang H. Hybrid intrusion detection system for Internet of Things (IoT). J ISMAC. 2020;2(4):190-9.

Eskandari M, Janjua ZH, Vecchio M, Antonelli F. Passban IDS: an intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet Things J. 2020;7(8):6882-97.

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

Kumar V, Das AK, Sinha D. UIDS: a unified intrusion detection system for IoT environment. Evol Intel. 2021;14(1):47-59.

Yang A, Zhuansun Y, Liu C, Li J, Zhang C. Design of intrusion detection system for Internet of Things based on improved BP neural network. IEEE Access. 2019;7:106043-52.

Derhab A, Aldweesh A, Emam AZ, Khan FA. Intrusion detection system for Internet of Things based on temporal convolution neural network and efficient feature engineering. Wireless Communications and Mobile Computing. 2020;2020:1-16.

Lo WW, Layeghy S, Sarhan M, Gallagher M, Portmann M. E-GraphSAGE: a graph neural network based intrusion detection system for IoT. NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium; 2022 Apr 25-29; Budapest, Hungary. USA: IEEE; 2022. p. 1-9.

Gassais R, Ezzati-Jivan N, Fernandez JM, Aloise D, Dagenais MR. Multi-level host-based intrusion detection system for Internet of Things. J Cloud Comp. 2020;9(1):1-16.

Abbas A, Khan MA, Latif S, Ajaz M, Shah AA, Ahmad J. A new ensemble-based intrusion detection system for Internet of Things. Arab J Sci Eng. 2022;47(2):1805-19.

Qureshi AuH, Larijani H, Ahmad J, Mtetwa N. A heuristic intrusion detection system for Internet-of-Things (IoT). In: Arai K, Bhatia R, Kapoor S, editors. Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing vol. 997. Cham: Springer; 2019. p. 86-98.

Davahli A, Shamsi M, Abaei G. Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks. J Ambient Intell Human Comput. 2020;11(11):5581-609.

Yan Q, Huang W, Luo X, Gong Q, Yu FR. A multi-level DDoS mitigation framework for the industrial Internet of Things. IEEE Commun Mag. 2018;56(2):30-6.

Frustaci M, Pace P, Aloi G, Fortino G. Evaluating critical security issues of the IoT world: present and future challenges. IEEE Internet Things J. 2018;5(4):2483-95.

Asghar MR, Dán G, Miorandi D, Chlamtac I. Smart meter data privacy: a survey. IEEE Commun Surv Tutor. 2017;19(4):2820-35.

Eckhoff D, Wagner I. Privacy in the smart city—applications, technologies, challenges, and solutions. IEEE Commun Surv Tutor. 2018;20(1):489-516.

Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, et al. Machine learning and deep learning methods for cybersecurity. IEEE Access. 2018;6:35365-81.

Soursos S, Žarko IP, Zwickl P, Gojmerac I, Bianchi G, Carrozzo G. Towards the cross-domain interoperability of IoT platforms. European conference on networks and communications (EuCNC); 2016 Jun 27-30; Athens, Greece. USA: IEEE; 2016. p. 398-402.

Cerullo G, Mazzeo G, Papale G, Ragucci B, Sgaglione L. Chapter 4 - IoT and sensor networks security. In: Ficco M, Palmieri F, editors. Security and Resilience in Intelligent Data-Centric Systems and Communication Networks. United States: Academic press; 2018. p. 77-101.

Xiao L, Wan X, Lu X, Zhang Y, Wu D. IoT security techniques based on machine learning: How do IoT devices use AI to enhance security? IEEE Signal Process Mag. 2018;35(5):41-9.

Venturebeat. Report: More than 1B IoT attacks in 2021 [Internet]. 2022 [cited 2022 Jun 17]. Available from: https://venturebeat.com/2022/04/25/report-more-than-1b-iot-attacks-in-2021/.