Enhancing IOT Security: Leveraging Artificial Intelligence
DOI:
https://doi.org/10.48001/978-81-966500-6-3-4Keywords:
Internet of Things (IoT), Cybersecurity, Artificial Intelligence (AI), Algorithms, Networks, Systems Adversarial AIAbstract
In recent years, the adoption of the Internet of Things (IoT) has experienced a rapid surge, accompanied by a corresponding rise in cybersecurity concerns. At the forefront of cybersecurity advancements lies Artificial Intelligence (AI), utilized for crafting sophisticated algorithms aimed at fortifying networks and systems, including those within the IoT realm. Nonetheless, cyber adversaries have identified methods to exploit AI, going as far as employing adversarial AI techniques to orchestrate cybersecurity breaches. This review paper consolidates insights from numerous surveys and scholarly works pertaining to IoT, AI, and AI-driven attacks, delving into the intricate interplay among these domains. The primary aim is to comprehensively synthesize and summarize pertinent literature in these areas, shedding light on the evolving landscape of IoT, AI, and cybersecurity, both in terms of defensive
strategies and offensive tactics employed by malicious actors.
Downloads
References
Aboelwafa, M. M., Seddik, K. G., Eldefrawy, M. H., Gadallah, Y., & Gidlund, M. (2020). A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT. IEEE Internet of Things Journal, 7(9), 8462–8471. https://doi.org/10.1109/JIOT.2020.2991693
Alahmadi, A. A., Aljabri, M., Alhaidari, F., Alharthi, D. J., Rayani, G. E., Marghalani, L. A., Alotaibi, O. B., & Bajandouh, S. A. (2023). DDoS Attack Detection in IoT-Based Networks Using Machine Learning Models: A Survey and Research Directions. Electronics (Switzerland), 12(14). https://doi.org/10.3390/electronics12143103
Cäsar, M., Pawelke, T., Steffan, J., & Terhorst, G. (2022). A survey on Bluetooth Low Energy security and privacy. Computer Networks, 205. https://doi.org/10.1016/j.comnet.2021.108712
Cohen, T., & Widdows, D. (2014). Geometric Representations in Biomedical Informatics: Applications in Automated Text Analysis. Methods in Biomedical Informatics: A Pragmatic Approach, 99–139. https://doi.org/10.1016/B978-0-12-401678-1.00005-1
Džaferović, E., Sokol, A., Almisreb, A. A., & Mohd Norzeli, S. (2019). DoS and DDoS vulnerability of IoT: A review. Sustainable Engineering and Innovation, 1(1), 43– 48. https://doi.org/10.37868/sei.v1i1.36
Gautam, S., & Mittal, P. (2022). Systematic Analysis of Predictive Modeling Methods in Stock Markets. International Research Journal of Computer Science, 9(11), 377– 385. https://doi.org/10.26562/irjcs.2022.v0911.01
Hallman, R., Bryan, J., Palavicini, G., Divita, J., & Romero-Mariona, J. (2017). IoDDoS -The internet of distributed denial of sevice attacks A case study of the mirai malware and IoT-Based botnets. IoTBDS 2017 - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security, 47–58. https://doi.org/10.5220/0006246600470058
Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97. https://doi.org/10.1016/j.inffus.2023.101804
Kiran. (2019). Internet of Things. In D. R. Kiran (Ed.), Production planning and control (pp. 495–513). Butterworth-Heinemann. https://doi.org/10.1016/B978-0-12-818364-9.00035-4
Kuzlu, M., Fair, C., & Guler, O. (2021). Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity. Discover Internet of Things. https://doi.org/10.1007/s43926-020-00001-4
Mehta, K., Mittal, P., Gupta, P. K., & Tandon, J. K. (2022). Analyzing the Impact of Forensic Accounting in the Detection of Financial Fraud: The Mediating Role of Artificial Intelligence. Advances in Intelligent Systems and Computing, 585–592.https://doi.org/10.1007/978-981-16-2597-8_50
Melamed, T. (2018). An active man-in-The-middle attack on bluetooth smart devices.International Journal of Safety and Security Engineering, 8(2), 200–211. https://doi.org/10.2495/SAFE-V8-N2-200-211
Mittal, P., Kaur, A., & Gupta, P. K. (2021). THE MEDIATING ROLE of BIG DATA to INFLUENCE PRACTITIONERS to USE FORENSIC ACCOUNTING for FRAUD DETECTION. European Journal of Business Science and Technology, 7(1), 47–58. https://doi.org/10.11118/ejobsat.2021.009
Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Support Vector Machines and Support Vector Regression. In Multivariate statistical machine learning methods for genomic prediction (pp. 337–378). Springer, Cham. https://doi.org/10.1007/978-3-030-89010-0_9
Mukhtar, B. I., Elsayed, M. S., Jurcut, A. D., & Azer, M. A. (2023). IoT Vulnerabilities and Attacks: SILEX Malware Case Study. Symmetry, 15(11). https://doi.org/10.3390/sym15111978
Noman, H. A., & Abu-Sharkh, O. M. (2023). Code Injection Attacks in Wireless-Based Internet of Things (IoT): A Comprehensive Review and Practical Implementations. Sensors, 23(13). https://doi.org/10.3390/s23136067
Sasi, T., Lashkari, A. H., Lu, R., Xiong, P., & Iqbal, S. (2023). A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges. Journal of Information and Intelligence. https://doi.org/10.1016/j.jiixd.2023.12.001
Sivasankari, N., & Kamalakkannan, S. (2022). Detection and prevention of man-in-the middle attack in iot network using regression modeling. Advances in Engineering Software, 169. https://doi.org/10.1016/j.advengsoft.2022.103126
Song, Y. Y., & Lu, Y. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi.org/10.11919/j.issn.1002-0829.215044