Threat Detection In Artificial Intelligence: A Review
DOI:
https://doi.org/10.69980/ajpr.v28i1.813Keywords:
Adversarial Resilience, Context-Aware Detection, Dynamic Cognitive Threat Matrix (DCTM), Conscious Defense, Adaptive Anomaly AnticipationAbstract
As Artificial Intelligence systems proliferate across critical sectors-from healthcare and finance to national defense and autonomous infrastructure-their exposure to adversarial threats becomes an existential concern. This research proposes a paradigm shift in threat detection within AI systems by integrating context-aware self-reflection and adaptive anomaly anticipation into neural architectures. Moving beyond conventional static threat models, this work introduces a Dynamic Cognitive Threat Matrix (DCTM)-a meta-layer that enables AI systems to perceive, predict, and preempt threats based on evolving environmental and internal behavioral cues. The study leverages multi-modal data fusion, causal inference, and adversarial resilience training to build a system that not only detects threats post-occurrence but anticipates them in real time with minimal false positives. We also explore the philosophical and ethical dimensions of "conscious threat response" in machines, challenging the traditional boundaries of human-machine decision hierarchies. Through extensive experimentation on real-world AI deployments and zero-day attack simulations, this research aims to set a new foundation for self-defensive intelligence in AI ecosystems. The expected outcome is not merely a threat detection algorithm but a framework for conscious defense-an AI that can learn the intent behind threats, adapt its vulnerability model, and evolve with time. This work aspires to pioneer the next generation of secure AI, where threat detection is not a function, but a form of evolving awareness.
References
1. I. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in Proc. Int. Conf. Learn. Representations (ICLR), San Diego, CA, USA, 2015.
2. N. Papernot, P. McDaniel, and I. Goodfellow, “Transferability in machine learning: from phenomena to black-box attacks,” arXiv preprint arXiv:1605.07277, 2016.
3. N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proc. IEEE Symp. Security and Privacy (SP), San Jose, CA, USA, 2017, pp. 39–57.
4. W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” in Proc. 25th Annual Network and Distributed System Security Symp. (NDSS), San Diego, CA, USA, 2018.
5. B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern Recognit., vol. 84, pp. 317–331, Dec. 2018.
6. A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” arXiv preprint arXiv:1607.02533, 2016.
7. J. Li, F. Tramer, and N. Papernot, “Certified adversarial robustness with additive noise,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019, pp. 7156–7166.
8. M. Naseer, S. Khan, and F. Porikli, “A self-supervised approach for adversarial robustness,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 262–271.
9. Y. Zhang, P. Chen, and Z. Wang, “Adversarial examples detection via adversarial gradient directions,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 3217–3221.
10. S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “DeepFool: A simple and accurate method to fool deep neural networks,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 2574–2582.
11. H. Huang, Z. Xu, and D. Evans, “Learning to learn from mistakes: Robust adversarial training with meta-learning,” in Proc. 37th Int. Conf. Machine Learning (ICML), Vienna, Austria, 2020, pp. 446–456.
12. S. Chen, C. Liu, and B. Li, “Detecting adversarial examples using neural network models,” in Proc. IEEE Symp. Security and Privacy Workshops (SPW), San Francisco, CA, USA, 2018, pp. 1–8.
13. A. Athalye, N. Carlini, and D. Wagner, “Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,” in Proc. Int. Conf. Machine Learning (ICML), Stockholm, Sweden, 2018, pp. 274–283.
14. J. Su, D. V. Vargas, and K. Sakurai, “One pixel attack for fooling deep neural networks,” IEEE Trans. Evol. Comput., vol. 23, no. 5, pp. 828–841, Oct. 2019.
15. S. Bhagoji, D. Cullina, and P. Mittal, “Dimensionality reduction as defense against adversarial attacks,” arXiv preprint arXiv:1704.02654, 2017.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 American Journal of Psychiatric Rehabilitation
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License permitting all use, distribution, and reproduction in any medium, provided the work is properly cited.
This work is licensed under a 
