AI vs. AI in IoT Security: A Systematic Review of Autonomous Exploits and Defense Mechanisms
DOI:
https://doi.org/10.70162/mijarcse/2026/v12/i1/v12i101Keywords:
Artificial Intelligence, Internet of Things, Cybersecurity, AI-driven Intrusion Detection Systems, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, Adversarial Machine Learning, Reinforcement Learning, Generative Adversarial NetworksAbstract
Artificial intelligence (AI) empowered defense systems have evolved as a unique response to cyber security challenges occurring in the Internet of Things (IoT) as conventional security measures are ineffective against autonomous and adaptive threat manifestations. The paper studies the association between AI-driven offensive and defensive methods for protecting smart equipment within the AI vs AI concept. This includes systematic review aligned with PRISMA insights implemented with peer-reviews from databases including Scopus, Web of Science, IEEE Xplore, and ScienceDirect. The study involved generation of the ultimate sample of n=62 domain-based studies and it highlights the urgent requirement to develop a scalable, adaptive and strong AI framework with capabilities to manage emerging challenges of IoT security. Future research needs to emphasize on improving generalized models for reducing false positives and unifying lighter AI solutions for resource limited devices
References
Q. A. Al-Haija and S. A. Tamimi, “A State-of-the-Art Survey of Adversarial Reinforcement Learning for IoT Intrusion Detection,” Computers, Materials & Continua, vol. 87, no. 1, pp. 1–10, 2026, doi: 10.32604/cmc.2025.073540.
M. Almiani, A. AbuGhazleh, A. Al-Rahayfeh, S. Atiewi, and A. Razaque, “Deep recurrent neural network for IoT intrusion detection system,” Simulation Modelling Practice and Theory, vol. 101, p. 102031, May 2020, doi: 10.1016/j.simpat.2019.102031.
B. Alotaibi, “A Survey on Industrial Internet of Things Security: Requirements, Attacks, AI-Based Solutions, and Edge Computing Opportunities,” Sensors, vol. 23, no. 17, p. 7470, Aug. 2023, doi: 10.3390/s23177470.
A. Alourani, M. Alam, A. Ali, I. R. Khan, and C. K. Samal, “Hybrid AI-IoT Framework with Digital Twin Integration for Predictive Urban Infrastructure Management in Smart Cities,” Computers, Materials & Continua, vol. 86, no. 1, pp. 1–32, 2026, doi: 10.32604/cmc.2025.070161.
U. E. Chigbu, S. O. Atiku, and C. C. Du Plessis, “The Science of Literature Reviews: Searching, Identifying, Selecting, and Synthesising,” Publications, vol. 11, no. 1, p. 2, Jan. 2023, doi: 10.3390/publications11010002.
F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine Learning in IoT Security: Current Solutions and Future Challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686–1721, 2020, doi: 10.1109/comst.2020.2986444.
Sridevi Kakolu, Muhammad Ashraf Faheem, and Muhammad Aslam, “AI-enabled intrusion detection systems in IoT networks: Advancing defense mechanisms for resource-constrained devices,” International Journal of Science and Research Archive, vol. 9, no. 1, pp. 752–769, Jun. 2023, doi: 10.30574/ijsra.2023.9.1.0316.
B. R. Kikissagbe and M. Adda, “Machine Learning-Based Intrusion Detection Methods in IoT Systems: A Comprehensive Review,” Electronics, vol. 13, no. 18, p. 3601, Sep. 2024, doi: 10.3390/electronics13183601.
T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated Learning: Challenges, Methods, and Future Directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, May 2020, doi: 10.1109/msp.2020.2975749.
D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” International Journal of Surgery, vol. 8, no. 5, pp. 336–341, 2010, doi: 10.1016/j.ijsu.2010.02.007.
C. Alioanei and N. Popescu, “AI-Based Solutions for Security and Resource Optimization in IoT Environments: A Systematic Review,” Information, vol. 16, no. 10, p. 841, Sep. 2025, doi: 10.3390/info16100841.
A. A. Selcuk, “A Guide for Systematic Reviews: PRISMA,” Turkish Archives of Otorhinolaryngology, vol. 57, no. 1, pp. 57–58, May 2019, doi: 10.5152/tao.2019.4058.
C. Zhang, X. Costa-Perez, and P. Patras, “Adversarial Attacks Against Deep Learning-Based Network Intrusion Detection Systems and Defense Mechanisms,” IEEE/ACM Transactions on Networking, vol. 30, no. 3, pp. 1294–1311, Jun. 2022, doi: 10.1109/tnet.2021.3137084.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). You may share and adapt the work for non-commercial purposes with appropriate attribution. For more details, visit https://creativecommons.org/licenses/by-nc/4.0/.
