Behavioral Analysis with AI for Detecting Fraudulent Activities in Web Applications
DOI:
https://doi.org/10.56472/ICCSAIML25-151Keywords:
Fraud detection, Artificial Intelligence, Behavioral analysis, Machine learning, Cybersecurity, Web applications, Anomaly detection, User behavior analytics, Transaction monitoring, Deep learningAbstract
Fraudulent activities in web applications have increased significantly, posing a substantial threat to businesses, users, and online systems. Traditional fraud detection mechanisms, such as rule-based filtering, have proven insufficient in dealing with the evolving nature of cyber fraud. Artificial Intelligence (AI), particularly behavioral analysis using machine learning, has emerged as a promising solution to detect and prevent fraudulent activities effectively. This paper explores the implementation of AI-driven behavioral analysis techniques for fraud detection in web applications. It discusses how machine learning models analyze user interactions, login behaviors, browsing patterns, and transaction anomalies to identify fraudulent activities. Furthermore, this study presents various AI algorithms, including supervised, unsupervised, and reinforcement learning approaches, highlighting their advantages and limitations. A comparative analysis of real-world case studies showcases the effectiveness of AI in fraud prevention. The study concludes with insights into the future potential of AI-driven fraud detection and recommendations for organizations to enhance security using behavioral analysis
Downloads
References
[1] Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602–613.
[2] B. C. C. Marella and D. Kodi, “Generative AI for fraud prevention: A new frontier in productivity and green innovation,” In Advances in Environmental Engineering and Green Technologies, IGI Global, 2025, pp. 185–200
[3] Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235–255.
[4] Gopichand Vemulapalli, Padmaja Pulivarthy, “Integrating Green Infrastructure With AI-Driven Dynamic Workload Optimization: Focus on Network and Chip Design,” in Integrating Blue-Green Infrastructure Into Urban Development, IGI Global, USA, pp. 397-422, 2025.
[5] S. Panyaram, "Automation and Robotics: Key Trends in Smart Warehouse Ecosystems," International Numeric Journal of Machine Learning and Robots, vol. 8, no. 8, pp. 1-13, 2024.
[6] Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
[7] P. K. Maroju, "Empowering Data-Driven Decision Making: The Role of Self-Service Analytics and Data Analysts in Modern Organization Strategies," International Journal of Innovations in Applied Science and Engineering (IJIASE), vol. 7, Aug. 2021.
[8] Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569.
[9] Mohanarajesh, Kommineni (2024). Develop New Techniques for Ensuring Fairness in Artificial Intelligence and ML Models to Promote Ethical and Unbiased Decision-Making. International Journal of Innovations in Applied Sciences and Engineering 10 (1):47-59.
[10] Animesh Kumar, “Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML)”, Transactions on Engineering and Computing Sciences, 12(4), 59-69. 2024.
[11] D. Kodi and S. Chundru, “Unlocking new possibilities: How advanced API integration enhances green innovation and equity,” In Advances in Environmental Engineering and Green Technologies, IGI Global, 2025, pp. 437–460
[12] Pronaya Bhattacharya Lakshmi Narasimha Raju Mudunuri, 2024, “Ethical Considerations Balancing Emotion and Autonomy in AI Systems”, Humanizing Technology With Emotional Intelligence, pp. 443-456.
[13] Carcillo, F., Dal Pozzolo, A., Le Borgne, Y. A., Caelen, O., Mazzer, Y., & Bontempi, G. (2018). Scarff: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41, 182–194.
[14] R. Daruvuri, “Dynamic load balancing in AI-enabled cloud infrastructures using reinforcement learning and algorithmic optimization,” World Journal of Advanced Research and Reviews, vol. 20, no. 1, pp. 1327–1335, Oct. 2023, doi: 10.30574/wjarr.2023.20.1.2045.
[15] Dal Pozzolo, A., Caelen, O., Johnson, R. A., & Bontempi, G. (2015). Calibrating probability with undersampling for unbalanced classification. 2015 IEEE Symposium Series on Computational Intelligence (SSCI).
[16] Predictive Assessment of Electric Vehicle (EV) Charging Impacts on Grid Performance - Sree Lakshmi Vineetha Bitragunta - IJLRP Volume 5, Issue 7, July 2024, PP-1-10, DOI 10.5281/zenodo.14945783.
[17] Ashima Bhatnagar Bhatia Padmaja Pulivarthi, (2024). Designing Empathetic Interfaces Enhancing User Experience Through Emotion. Humanizing Technology With Emotional Intelligence. 47-64. IGI Global.
[18] Sethi, T., Kantardzic, M., & Ouyang, C. (2017). A reinforcement learning approach to adaptive fraud detection. Proceedings of the 26th International Conference on World Wide Web Companion, 1281–1289.
[19] Sahil Bucha, “Design And Implementation of An AI-Powered Shipping Tracking System For E-Commerce Platforms”, Journal of Critical Reviews, Vol 10, Issue 07, 2023, Pages. 588-596.
[20] Kodi, D. (2024). “Automating Software Engineering Workflows: Integrating Scripting and Coding in the Development Lifecycle “. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 635–652.
[21] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1–58.
[22] Sandeep Sasidharakarnavar. “Enhancing HR System Agility through Middleware Architecture”. IJAIBDCMS [International JournalofAI,BigData,ComputationalandManagement Studies]. 2025 Mar. 14 [cited 2025 Jun. 4]; 6(1):PP. 89-97.
[23] V. M. Aragani, "Evaluating Reinforcement Learning Agents for Portfolio Management," 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2025, pp. 1-6, doi: 10.1109/ICAECT63952.2025.10958880.
[24] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
[25] Bhagath Chandra Chowdari Marella, “Driving Business Success: Harnessing Data Normalization and Aggregation for Strategic Decision-Making”, International Journal of Intelligent Systems And Applications In Engineering, vol. 10, no.2, pp. 308 – 317, 2022. https://ijisae.org/index.php/IJISAE/issue/view/87
[26] Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 1310–1321.
[27] Kotte, K. R., & Panyaram, S. (2025). Supply Chain 4.0: Advancing Sustainable Business. Driving Business Success Through Eco-Friendly Strategies, 303.
[28] Settibathini, V. S., Kothuru, S. K., Vadlamudi, A. K., Thammreddi, L., & Rangineni, S. (2023). Strategic analysis review of data analytics with the help of artificial intelligence. International Journal of Advances in Engineering Research, 26, 1-10.
[29] Puvvada, R. K. "SAP S/4HANA Cloud: Driving Digital Transformation Across Industries." International Research Journal of Modernization in Engineering Technology and Science 7.3 (2025): 5206-5217.
[30] Jagadeesan Pugazhenthi, V., Singh, J., & Pandy, G. (2025). Revolutionizing IVR Systems with Generative AI for Smarter Customer Interactions. International Journal of Innovative Research in Computer and Communication Engineering, 13(1).
[31] Multiconnected Interleaved Boost Converter for Hybrid Energy System, Sree Lakshmi Vineetha Bitragunta, International Journal of Scientific Research in Engineering and Management (Ijsrem), Volume: 08 Issue: 03 | March – 2024, Pp-1-9.
[32] A. Garg, “Unified Framework of Blockchain and AI for Business Intelligence in Modern Banking ”, IJERET, vol. 3, no. 4, pp. 32–42, Dec. 2022, doi: 10.63282/3050-922X.IJERET-V3I4P105
