High-Frequency Trading Platform Real-Time Cyber Threat Analysis

Authors

  • Omar Farooq Research Fellow, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia Author

Keywords:

Distributed AI models, real-time threat analysis, high-frequency trading, scalability

Abstract

HFT system makes huge number of transactions in microseconds, making them vulnerable to security and cyber threat. This research paper aims to study about distributed AI models for real time cyber threat detection. HFT system have complex trading algorithm slowness, scalability, and threat detection. Various machine learning algorithms like Federated, Reinforcement, and Deep learning models are being tested to overcome these cyber security threats.

Downloads

Download data is not yet available.

References

Lee, J., & Chen, X. (2020). Optimizing deep learning for cybersecurity in high-frequency trading platforms. Journal of Financial Technology, 8(3), 123-135.

Gupta, A., & Sharma, R. (2021). Real-time threat detection using federated learning in high-frequency trading. Cybersecurity Innovations, 16(2), 45-56.

Zhao, L., & Wang, Y. (2022). Reinforcement learning in high-frequency trading cybersecurity. AI & Finance Journal, 19(4), 200-215.

Singh, S., & Kumar, A. (2021). Distributed machine learning models for cyber threat analysis in trading platforms. Journal of Financial Markets, 24(2), 175-189.

Patel, M., & Gupta, S. (2020). Improving scalability in federated learning for cybersecurity applications. Journal of Distributed Systems, 14(3), 99-111.

V. Pillai, “Anomaly Detection in Financial and Insurance Data-Systems”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 144–183, Sep. 2024

Sivaraman, Hariprasad. "Intelligent Code Coverage Optimization Using Machine Learning for Large Scale Systems." International Journal for Multidisciplinary Research 5.5 (2023).

S. Kumari, “Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response ”, Adv. in Deep Learning Techniques, vol. 3, no. 2, pp. 50–74, Dec. 2023

Singu, Santosh Kumar. "Migration strategies for legacy data warehousing systems to cloud platforms." Internafional Journal of Science and Research (IJSR) 12, no. 12 (2023): 2164-2167.

Tamanampudi, Venkata Mohit. "AI Agents in DevOps: Implementing Autonomous Agents for Self-Healing Systems and Automated Deployment in Cloud Environments." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 507-556.

Machireddy, Jeshwanth Reddy, and Harini Devapatla. "Enhancing Predictive Analytics with AI-Powered RPA in Cloud Data Warehousing: A Comparative Study of Traditional and Modern Approaches." Journal of Deep Learning in Genomic Data Analysis 3.1 (2023): 74-99.

Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.

Pillai, V. “Data Analytics and Engineering in Automobile Data Systems”. Journal of Science & Technology, vol. 4, no. 6, Dec. 2023, pp. 140-79, https://thesciencebrigade.com/jst/article/view/520

S. Kumari, “Leveraging AI for Cybersecurity in Agile Cloud-Based Platforms: Real-Time Anomaly Detection and Threat Mitigation in DevOps Pipelines”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 698–715, May 2023

Alam, Khorshed, et al. "Designing Autonomous Carbon Reduction Mechanisms: A Data-Driven Approach in Renewable Energy Systems." Well Testing Journal 32.2 (2023): 103-129.

Sivaraman, Hariprasad. (2023). A Machine Learning Paradigm for Cross-Sector Financial Crime Prevention. 14.

Ravichandran, Prabu, Jeshwanth Reddy Machireddy, and Sareen Kumar Rachakatla. "Data Analytics Automation with AI: A Comparative Study of Traditional and Generative AI Approaches." Journal of Bioinformatics and Artificial Intelligence 3.2 (2023): 168-190.

Pillai, Vinayak. “Implementing Efficient Data Operations: An Innovative Approach”. Asian Journal of Multidisciplinary Research & Review, vol. 3, no. 6, Dec. 2022, pp. 231-67, https://ajmrr.org/journal/article/view/241.

Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.

S. Kumari, “AI-Driven Product Management Strategies for Enhancing Customer-Centric Mobile Product Development: Leveraging Machine Learning for Feature Prioritization and User Experience Optimization ”, Cybersecurity & Net. Def. Research, vol. 3, no. 2, pp. 218–236, Nov. 2023.

Al Imran, Md, Abdullah Al Fathah, Abdullah Al Baki, Khorshed Alam, Md Ali Mostakim, Upal Mahmud, and M. S. Hossen. "Integrating IoT and AI For Predictive Maintenance in Smart Power Grid Systems to Minimize Energy Loss and Carbon Footprint." Journal of Applied Optics 44, no. 1 (2023): 27-47.

Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.

S. Kumari, “AI-Powered Agile Project Management for Mobile Product Development: Enhancing Time-to-Market and Feature Delivery Through Machine Learning and Predictive Analytics”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 342–360, Dec. 2023

Alam, K., M. A. Mostakim, A. A. Baki, and M. S. Hossen. "CURRENT TRENDS IN PHOTOVOLTAIC THERMAL (PVT) SYSTEMS: A REVIEW OF TECHNOLOGIES AND SUSTAINABLE ENERGY SOLUTIONS." Academic Journal on Business Administration, Innovation & Sustainability 4, no. 04 (2024): 128-143.

Kim, Y., & Lee, K. (2022). Application of deep learning in detecting market manipulation. Financial Data Science Review, 3(1), 50-65.

Huang, Z., & Lin, H. (2021). Adaptive AI models for real-time cybersecurity in trading. Journal of Trading Systems, 12(4), 98-112.

Downloads

Published

10-01-2024

How to Cite

[1]
Omar Farooq, “High-Frequency Trading Platform Real-Time Cyber Threat Analysis”, American J Data Sci Artif Intell Innov, vol. 4, pp. 1–6, Jan. 2024, Accessed: Mar. 07, 2026. [Online]. Available: https://ajdsai.org/index.php/publication/article/view/7