The ever-increasing use of IoT devices has presented new security threats and, thus, requires IDS to protect interconnected IoT networks. This paper identifies the use of ML and DL as a new effective way of improving protection against cyber threats in IoT networks. This review aims to review the most up-to-date research on ML and DL techniques for IoT-based IDS concerning responding to new threats like zero-day attacks and Distributed Denial of Service (DDoS). Thus, the review uses twenty cognate peer-reviewed studies published between 2023 and 2024 to emphasize the methodological variability, datasets, as well as the forms of performance metrics present in the field. The outcomes show that four modern frameworks, including hybrid models, federated learning, LSTMs, and convolutional architectures, perform much better than conventional methods in terms of accuracy, detection rates, and false-positive rates. For example, models such as feature selection employing new paradigms like the inclusion of new features in the suite, cost-sensitive learning, as well as multitask paradigms show better scalability and flexibility to handle imbalanced datasets as well as cyber-attacks of other unseen types. Also, the incorporation of IDS with energy-efficient protocols and fog computing more real-time capability of such systems within IoT-constrained resources network. However, a number of limitations including computational complexity, privacy issues, and lack of a proper baseline for model comparison remain major ongoing issues. Overall, this review consolidates important ideas about the advantages and weaknesses of current methods together with directions for further investigation such as the practices of federated deep learning, adaptive algorithms, and real-time anomaly detection frameworks. In conclusion, this paper establishes the importance of ML and DL in enhancing the robustness of IoT systems to the increasing ecosystem of cybersecurity threats.
References
[1]
Sicari, S., Rizzardi, A., Grieco, L.A. and Coen-Porisini, A. (2015) Security, Privacy and Trust in Internet of Things: The Road Ahead. Computer Networks, 76, 146-164. https://doi.org/10.1016/j.comnet.2014.11.008
[2]
Berman, D.S., Buczak, A.L., Chavis, J.S. and Corbett, C.L. (2019) A Survey of Deep Learning Methods for Cyber Security. Information, 10, Article 122. https://doi.org/10.3390/info10040122
[3]
Elsayed, R., Hamada, R., Hammoudeh, M., Abdalla, M. and Elsaid, S.A. (2022) A Hierarchical Deep Learning-Based Intrusion Detection Architecture for Clustered Internet of Things. Journal of Sensor and Actuator Networks, 12, Article 3. https://doi.org/10.3390/jsan12010003
[4]
Telikani, A., Rudbardeh, N.E., Soleymanpour, S., Shahbahrami, A., Shen, J., Gaydadjiev, G., et al. (2024) A Cost-Sensitive Machine Learning Model with Multitask Learning for Intrusion Detection in IOT. IEEE Transactions on Industrial Informatics, 20, 3880-3890. https://doi.org/10.1109/tii.2023.3314208
[5]
Laha, B., Basu, D., Biswas, S., Gupta, P. and Sadhukhan, B. (2023) Intrusion Detection in IOT Systems Using Ensemble Machine Learning Techniques. 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON), Mysore, 5-7 August 2023, 1-7. https://doi.org/10.1109/indiscon58499.2023.10270505
[6]
Panthakkan, A., Anzar, S.M. and Mansoor, W. (2023) Enhancing IOT Security: A Machine Learning Approach to Intrusion Detection System Evaluation. 2023 IEEE International Conference and Expo on Real Time Communications at IIT (RTC), Chicago, 2-5 October 2023, 19-23. https://doi.org/10.1109/rtc58825.2023.10304239
[7]
Khan, I.U., Ayub, M.Y., Abdollahi, A. and Dutta, A. (2023) A Hybrid Deep Learning Model-Based Intrusion Detection System for Emergency Planning Using IOT-Network. 2023 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Cosenza, 13-15 September 2023, 1-5. https://doi.org/10.1109/ict-dm58371.2023.10286954
[8]
Radjaa, B., Nabila, L. and Salameh, H.B. (2023) Federated Deep Learning-Based Intrusion Detection Approach for Enhancing Privacy in Fog-IOT Networks. 2023 10th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), San Antonio, 23-25 October 2023, 156-160. https://doi.org/10.1109/iotsms59855.2023.10325826
[9]
Ennaji, E.M., El Hajla, S., Maleh, Y. and Mounir, S. (2024) Federated Deep Learning Models for Intrusion Detection in IOT. Proceedings of the 7th International Conference on Networking, Intelligent Systems and Security, Meknes, 18-19 April 2024, 1-5. https://doi.org/10.1145/3659677.3659837
[10]
Walling, S. and Lodh, S. (2024) Enhancing IOT Intrusion Detection through Machine Learning with AN-SFS: A Novel Approach to High Performing Adaptive Feature Selection. Discover Internet of Things, 4, Article No. 6. https://doi.org/10.1007/s43926-024-00074-5
[11]
Francis, G., Sheeja, S. and John, A. (2023) IOT Intrusion Detection Using the Two-Tier-Convolutional Deep-Learning Model. 2023 International Conference on IOT, Communication and Automation Technology (ICICAT), Gorakhpur, 23 June 2023, 1-7.
[12]
Divakarla, U. and Chandrasekaran, K. (2023) IOT Devices Using Supervised Machine Learning Models for Anomaly Based Intrusion Detection. 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, 1-3 March 2023, 1-5. https://doi.org/10.1109/esci56872.2023.10099676
[13]
Sharma, A. and Babbar, H. (2023) Enhancing IOT Security: Machine Learning-Based Network Intrusion Detection. 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet, 25-27 August 2023, 1-6. https://doi.org/10.1109/asiancon58793.2023.10269850
[14]
Wadate, A.J. and Deshpande, S.P. (2023) A Deep Machine Learning Approach for Intrusion Detection in IOT. 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, 3-4 November 2023, 58-63. https://doi.org/10.1109/icccis60361.2023.10425682
[15]
Diallo, A., Affognon, L., Diallo, C. and Ezin, E.C. (2023) A Three-Level Deep Learning Intrusion Detection System for IOT Network. 2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE), Dubai, 30-31 December 2023, 1-6. https://doi.org/10.1109/icecce61019.2023.10442798
[16]
Tong, J. and Zhang, Y. (2024) A Real-Time Label-Free Self-Supervised Deep Learning Intrusion Detection for Handling New Type and Few-Shot Attacks in IOT Networks. IEEE Internet of Things Journal, 11, 30769-30786. https://doi.org/10.1109/jiot.2024.3414492
[17]
Kumar, M. and Dubey, S.K. (2023) Network Intrusion Detection for IOT Devices Using Deep Learning. 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Gautam Buddha Nagar, 1-3 December 2023, 1198-1203. https://doi.org/10.1109/upcon59197.2023.10434703
[18]
Hinojosa, A. and Majd, N.E. (2024) Edge Computing Network Intrusion Detection System in IOT Using Deep Learning. 2024 33rdInternational Conference on Computer Communications and Networks (ICCCN), Kailua-Kona, 29-31 July 2024, 1-6. https://doi.org/10.1109/icccn61486.2024.10637611
[19]
Chauhan, D., Shah, M. and Joshi, H. (2023) A Novel Intrusion Detection System Based on Machine Learning for Internet of Things (IOT) Devices. 2023 3rd International Conference on Smart Data Intelligence (ICSMDI), Trichy, 30-31 March 2023, 427-434. https://doi.org/10.1109/icsmdi57622.2023.00081
[20]
Mouiti, M., Elhariri, A., Habibi, O. and Lazaar, M. (2023) Toward Improving Internet of Things (IOT) Networks Security Using Machine Learning Based Intrusion Detection System. 2023 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), Casablanca, 3-5 May 2023, 46-51. https://doi.org/10.1109/icdata58816.2023.00018
[21]
Elshweikh, A.A., Maher, A.M., Hussein, M. and Elbayoumy, A.D. (2024) Intrusion Detection System for IOT Using Machine Learning. 2024 International Telecommunications Conference (ITC-Egypt), Cairo, 22-25 July 2024, 326-331. https://doi.org/10.1109/itc-egypt61547.2024.10620546
[22]
Mahdi, M.A. (2024) Secure and Efficient IOT Networks: An AI and Ml-Based Intrusion Detection System. 2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT), Vellore, 3-4 May 2024, 1-6. https://doi.org/10.1109/aiiot58432.2024.10574789
