AI-Driven Cybersecurity Software for Fraud and Network Intrusion Detection in Financial Big Data Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0806019Keywords:
AI, Machine Learning, Cybersecurity, Big Data, Network Intrusion Detection, Fraud Detection, Financial Systems, Banking Security, Trading Systems, Anomaly Detection, Predictive Analytics, Real-Time MonitoringAbstract
The rapid growth of digital banking and financial trading systems has significantly increased the risk of cyber threats, including network intrusions and fraudulent activities. Traditional security mechanisms often struggle to handle the high volume, velocity, and variety of financial big data, leading to delayed threat detection and financial losses. This research proposes an AI-driven cybersecurity software framework that leverages machine learning algorithms, big data analytics, and real-time monitoring to detect and prevent fraudulent transactions and network intrusions. The proposed system integrates anomaly detection, predictive analytics, and automated threat response to enhance security, reduce false positives, and improve operational efficiency. By combining scalable big data processing with intelligent machine learning models, the framework ensures proactive and adaptive cybersecurity for banking and financial trading platforms. Experimental results on real-world financial datasets demonstrate high accuracy, robustness, and efficiency, highlighting the potential of AI-powered solutions in safeguarding critical financial infrastructures.References
1. Denning, D. E. (1987). An intrusion-detection model. IEEE TSE.
2. Vinay, T. M., Sunil, M., & Anand, L. (2024, April). IoTRACK: An IoT based'Real-Time'Orbiting Satellite Tracking System. In 2024 2nd International Conference on Networking and Communications (ICNWC) (pp. 1-6). IEEE.
3. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
4. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
5. Padmanabham, S. (2025). Security and Compliance in Integration Architectures: A Framework for Modern Enterprises. International Journal of Computing and Engineering, 7(16), 45-55.
6. Cheekati, S., Borra, C. R., Pareek, P. K., Rayala, R. V., Kowsalya, S. S. N., & Selvam, J. (2025, May). Cybersecurity Threat Detection Using OpCyNet and DBRA: A Deep Learning Approach for DDoS Attack Mitigation on CICDDoS2019. In 2025 13th International Conference on Smart Grid (icSmartGrid) (pp. 782-788). IEEE.
7. Christadoss, J., Kalyanasundaram, P. D., & Vunnam, N. (2024). Hybrid GraphQL-FHIR Gateway for Real-Time Retail-Health Data Interchange. Essex Journal of AI Ethics and Responsible Innovation, 4, 204-238.
8. Ponnoju, S. C., & Paul, D. (2023, April 3). Hybridizing Apache Camel and Spring Boot for Next-Generation microservices in financial data integration. https://lajispr.org/index.php/publication/article/view/37
9. Rahman, M. R., Tohfa, N. A., Arif, M. H., Zareen, S., Alim, M. A., Hossen, M. S., ... & Bhuiyan, T. (2025). Enhancing android mobile security through machine learning-based malware detection using behavioral system features.
10. Rajurkar, P. (2025). An AI-Driven Framework for Real-Time Fenceline Monitoring to Proactively Detect and Mitigate Hazardous Air Pollutants (HAPs). Journal ISSN, 1929, 2732.
11. Khan, M. I. (2025). Big Data Driven Cyber Threat Intelligence Framework for US Critical Infrastructure Protection. Asian Journal of Research in Computer Science, 18(12), 42-54.
12. Miriyala, N. S., Bandaru, B. K., Mittal, P., Macha, K. B., Venkat, R., & Rai, A. An Efficient Solution towards SDLC Automation using Multi-Agent Integration through Crew AI. https://www.researchgate.net/profile/Kiran-Babu-Macha-2/publication/392167255_An_Efficient_Solution_towards_SDLC_Automation_using_Multi-Agent_Integration_through_Crew_AI/links/6837cc946a754f72b58cc4b7/An-Efficient-Solution-towards-SDLC-Automation-using-Multi-Agent-Integration-through-Crew-AI.pdf
13. Nagarajan, G. (2022). Advanced AI–Cloud Neural Network Systems with Intelligent Caching for Predictive Analytics and Risk Mitigation in Project Management. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7774-7781.
14. Muthusamy, M. (2025). A Scalable Cloud-Enabled SAP-Centric AI/ML Framework for Healthcare Powered by NLP Processing and BERT-Driven Insights. International Journal of Computer Technology and Electronics Communication, 8(5), 11457-11462.
15. Vasugi, T. (2022). AI-Optimized Multi-Cloud Resource Management Architecture for Secure Banking and Network Environments. International Journal of Research and Applied Innovations, 5(4), 7368-7376.
16. Kumar, R. K. (2024). Real-time GenAI neural LDDR optimization on secure Apache–SAP HANA cloud for clinical and risk intelligence. IJEETR, 8737–8743. https://doi.org/10.15662/IJEETR.2024.0605006
17. Vijayaboopathy, V., Mathur, T., & Selvaraj, G. S. (2025). Generative AI Documentation of Dynamic IT Architectures. Newark Journal of Human-Centric AI and Robotics Interaction, 5, 178-214.
18. Surampudi, Y., Kondaveeti, D., & Pichaimani, T. (2023). A Comparative Study of Time Complexity in Big Data Engineering: Evaluating Efficiency of Sorting and Searching Algorithms in Large-Scale Data Systems. Journal of Science & Technology, 4(4), 127-165.
19. Kusumba, S. (2024). Data Integration: Unifying Financial Data for Deeper Insight. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 9939-9946.
20. Kanumarlapudi, P. K., Peram, S. R., & Kakulavaram, S. R. (2024). Evaluating Cyber Security Solutions through the GRA Approach: A Comparative Study of Antivirus Applications. International Journal of Computer Engineering and Technology (IJCET), 15(4), 1021-1040.
21. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
22. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
23. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
24. Sukla, R. R. (2025). Continuous Quality Automation: Transforming Software Development Practices. Journal Of Multidisciplinary, 5(7), 361-367.
25. Godleti, S. B. (2025). Taming Spark Data Skew with Practical Solutions. Journal of Computer Science and Technology Studies, 7(6), 752-758.
26. Nadiminty, Y. (2025). Accelerating Cloud Modernization with Agentic AI. Journal of Computer Science and Technology Studies, 7(9), 26-35.
27. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3 (5), 44–53.
28. Malarkodi, K. P., Sugumar, R., Baswaraj, D., Hasan, A., & Kousalya, A. (2023, March). Cyber Physical Systems: Security Technologies, Application and Defense. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 2536-2546). IEEE.
29. Singh, N. N. (2025). Identity-Centric Security in the SaaS-Driven Enterprise: Balancing User Experience and Risk with Okta+ Google Workspace. Journal of Computer Science and Technology Studies, 7(9), 87-96.
