Machine Learning Defenses: Protecting Financial Markets from Ai-Driven Attacks
DOI:
https://doi.org/10.32628/CSEIT251112378Keywords:
Machine Learning, Finance, AI, AttacksAbstract
This research focuses on exploring different machine learning defence strategies in protecting the financial market from attacks by AI. Based on the assessment of the main defence mechanisms, including adversarial training, anomaly detection, and model robustness, this work identifies strategies that help to minimize the threats linked to malicious AI manipulation. The results presented prove that adversarial training enhances model robustness at the cost of accuracy and that autoencoders with a suitable architecture are highly effective in detecting anomalous behaviour at the cost of high time consumption. It also identifies how the three factors; accuracy, latency and efficiency are all interrelated and trade-off against each other especially when developing models for real-time decision making in the complex financial world. In addition, updating the model regularly is described as crucial to sustaining strong protective measures across the duration of threats. In summary, this work offers several contributions for enhancing the understanding of how machine learning can be used to better prevent and combat future threats in financial markets, and presents a clear path for how this protective research can be advanced further.
Downloads
References
Goldblum, M., Schwarzschild, A., Patel, A., & Goldstein, T. (2021, November). Adversarial attacks on machine learning systems for high-frequency trading. In Proceedings of the Second ACM International Conference on AI in Finance (pp. 1-9). https://doi.org/10.1145/3490354.3494367
Boppiniti, S. T. (2021). Artificial Intelligence In Financial Markets: Algorithms And Applications. Available at SSRN. https://www.researchgate.net/profile/Sai-Teja-Boppiniti/publication/385034876_ARTIFICIAL_INTELLIGENCE_IN_FINANCIAL_MARKETS_ALGORITHMS_AND_APPLICATIONS/links/67611ef6a3978e15e7903de6/ARTIFICIAL-INTELLIGENCE-IN-FINANCIAL-MARKETS-ALGORITHMS-AND-APPLICATIONS.pdf
Azzutti, A., Ringe, W. G., & Stiehl, H. S. (2021). Machine learning, market manipulation, and collusion on capital markets: Why the" black box" matters. U. Pa. J. Int'l L., 43, 79. https://heinonline.org/HOL/LandingPage?handle=hein.journals/upjiel43&div=6&id=&page=
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2
Koshiyama, A., Firoozye, N., & Treleaven, P. (2020). Algorithms in future capital markets. Available at SSRN 3527511. https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3527511
Aldhyani, T. H., & Alzahrani, A. (2022). Framework for predicting and modeling stock market prices based on deep learning algorithms. Electronics, 11(19), 3149. https://doi.org/10.3390/electronics11193149
Zheng, X. L., Zhu, M. Y., Li, Q. B., Chen, C. C., & Tan, Y. C. (2019). FinBrain: when finance meets AI 2.0. Frontiers of Information Technology & Electronic Engineering, 20(7), 914-924. https://doi.org/10.1631/FITEE.1700822
Cao, L. (2020). AI in finance: A review. Available at SSRN 3647625. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3647625
Nicholls, J., Kuppa, A., & Le-Khac, N. A. (2021). Financial cybercrime: A comprehensive survey of deep learning approaches to tackle the evolving financial crime landscape. Ieee Access, 9, 163965-163986. https://doi.org/10.1109/ACCESS.2021.3134076
Khurana, N., Mittal, S., Piplai, A., & Joshi, A. (2019, October). Preventing poisoning attacks on AI based threat intelligence systems. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE. https://doi.org/10.1109/MLSP.2019.8918803
Milojević, N., & Redzepagic, S. (2021). Prospects of artificial intelligence and machine learning application in banking risk management. Journal of Central Banking Theory and Practice, 10(3), 41-57. https://intapi.sciendo.com/pdf/10.2478/jcbtp-2021-0023
Lee, O., Joo, H., Choi, H., & Cheon, M. (2022). Proposing an integrated approach to analyzing ESG data via machine learning and deep learning algorithms. Sustainability, 14(14), 8745. https://doi.org/10.3390/su14148745
Zohuri, B., & Rahmani, F. M. (2019). Artificial intelligence driven resiliency with machine learning and deep learning components. International Journal of Nanotechnology & Nanomedicine, 4(2), 1-8. https://www.davidpublisher.com/Public/uploads/Contribute/5e1c0da2a6587.pdf
Soni, V. D. (2019). Role of artificial intelligence in combating cyber threats in banking. International Engineering Journal For Research & Development, 4(1), 7-7. https://d1wqtxts1xzle7.cloudfront.net/63947012/1020200717-18417-d6nki8-libre.pdf?1594996672=&response-content-disposition=inline%3B+filename%3DROLE_OF_ARTIFICIAL_INTELLIGENCE_IN_COMBA.pdf&Expires=1736961167&Signature=ZJStrJP42OZXuFr0JVdWfAMDOJ9pKMS8NJESrtQnovKt1X1OI-m0yTcru0BAbGIdMQVEbqFrZ8B2PZFQvmJlWdMgOjp3d~eww71RlZzwjSGoPgW6S3xqHwfe5-uxm4AFmr~SsM-7B5ipsAdLXBYZxKm3tAjWMAEyihSmdPTbkovhiK6ZUos3pOSpsmcu4Oj81jxWHfICvEMYxkaeXjc2~7xWki-awevOIQ~s3uBmYJ24KHsseXwDSuW5iesLa~4CUNu5ErycF0aE6DQcnyqAUmlBQU~jBa10qBLMAkoItVUT7HYRhVE4XHX6Hce1qCcrbBstKZAYOMoj8yfFwMy~Vg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
Dang, C., Wang, F., Yang, Z., Zhang, H., & Qian, Y. (2022). RETRACTED ARTICLE: Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model. Operations Management Research, 15(3), 662-675. https://doi.org/10.1007/s12063-021-00252-6
Khan, H. U., Malik, M. Z., Alomari, M. K. B., Khan, S., Al-Maadid, A. A. S., Hassan, M. K., & Khan, K. (2022). Transforming the capabilities of artificial intelligence in GCC financial sector: a systematic literature review. Wireless communications and mobile computing, 2022(1), 8725767. https://doi.org/10.1155/2022/8725767
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.