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Dr.C.Dastagiraiah, B.Venkatesh, A.Tejaswini, E.Sruthi
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Page No: 1 - 12
Abstract : In today’s digital age, secure online transactions are crucial for maintaining the confidentiality, integrity, and authenticity of sensitive data. This project presents a Secure Online Transaction System developed in Java, utilizing MySQL as the database management system, and employing the AES (Advanced Encryption Standard) algorithm for encryption purposes. The objective of this project was to design and implement a robust system that ensures secure online transactions, safeguarding against unauthorized access, data breaches, and fraudulent activities. To achieve this, the project leveraged the AES algorithm, a widely adopted symmetric encryption algorithm known for its high level of security and performance. The system architecture includes a client-server model, where the clients are responsible for initiating and executing transactions, while the server manages the transaction requests and interacts with the MySQL database. The Java programming language was used to develop the client and server components, facilitating platform independence and ease of deployment. To secure the sensitive transaction data during transmission and storage, the AES algorithm was implemented. The AES algorithm operates on 128-bit blocks and supports key lengths of 128, 192, and 256 bits. It provides robust encryption and decryption functions, ensuring that the data remains confidential and tamper-proof. The keys used in the AES algorithm were securely generated and managed within the system. The MySQL database was employed to store transaction-related information, user credentials, and other relevant data. The integration of MySQL allowed for efficient data management and retrieval, with appropriate security measures implemented to protect against SQL injection attacks and unauthorized database access. The implemented Secure Online Transaction System with Cryptography successfully provides a secure environment for users to conduct online transactions. The utilization of the AES algorithm ensures the confidentiality and integrity of the transaction data, protecting it from unauthorized access and tampering. The systems integration with MySQL enables efficient and reliable data management, further enhancing the overall user experience. The outcomes of this project contribute to the field of secure online transactions by demonstrating the successful implementation of cryptography techniques using the AES algorithm. The developed system serves as a practical example of how Java, MySQL, and AES can be combined to create a robust and secure online transaction platform. The projects findings can benefit individuals, businesses, and financial institutions by providing them with a secure framework for conducting online transactions and protecting sensitive information.
Keyword Secure Online Transaction, Cryptography, AES Algorithm, Java, MySQL, Encryption, Decryption, Data Security, Authentication, Confidentiality, Integrity, Digital Payments, Cybersecurity Client-Server Model, Secure Data Transmission.
Reference:
    1. Reddy, C. N. K., & Murthy, G. V. (2012). Evaluation of Behavioral Security in Cloud Computing. International Journal of Computer Science and Information Technologies, 3(2), 3328-3333.
    2. Murthy, G. V., Kumar, C. P., & Kumar, V. V. (2017, December). Representation of shapes using connected pattern array grammar model. In 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 819-822). IEEE.
    3. Krishna, K. V., Rao, M. V., & Murthy, G. V. (2017). Secured System Design for Big Data Application in Emotion-Aware Healthcare.
    4. Rani, G. A., Krishna, V. R., & Murthy, G. V. (2017). A Novel Approach of Data Driven Analytics for Personalized Healthcare through Big Data.
    5. Rao, M. V., Raju, K. S., Murthy, G. V., & Rani, B. K. (2020). Configure and Management of Internet of Things. Data Engineering and Communication Technology, 163.
    6. Balakrishna, G., Murthy, G. V., Rao, M. N., & Narayana, M. V. (2022). Implementing Solar Power Smart Irrigation System. In Innovations in Computer Science and Engineering: Proceedings of the Ninth ICICSE, 2021 (pp. 561-567). Singapore: Springer Singapore.
    7. Reddy, S. R., & Murthy, G. V. (2025). Cardiovascular Disease Prediction Using Particle Swarm Optimization and Neural Network Based an Integrated Framework. SN Computer Science, 6(2), 186.
    8. Murthy, G. V., & Kumar, V. V. (2014). A new model of array grammar for generating connected patterns on an image neighborhood. arXiv preprint arXiv:1407.8337.
    9. Murthy, G. V., SwathiReddy, M., & Balakrishna, G. (2019, May). Big Data Analytics for Popularity Prediction. In Journal of Physics: Conference Series (Vol. 1228, No. 1, p. 012051). IOP Publishing.
    10. Kumar, K. M., Latha, P. S., & Murthy, G. V. (2017). Two Stage: Smart Crawler for Analysis of Web Data.
    11. Ramakrishna, C., Kumar, G. K., Reddy, A. M., & Ravi, P. (2018). A Survey on various IoT Attacks and its Countermeasures. International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), 5(4), 143-150.
    12. Madar, B., Kumar, G. K., & Ramakrishna, C. (2017). Captcha breaking using segmentation and morphological operations. International Journal of Computer Applications, 166(4), 34-38.
    13. Ramakrishna, C., Kumar, G. S., & Reddy, P. C. S. (2021). Quadruple band-notched compact monopole UWB antenna for wireless applications. Journal of Electromagnetic Engineering and Science, 21(5), 406-416.
    14. Chithanuru, V., & Ramaiah, M. (2023). An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions–A review. Concurrency and Computation: Practice and Experience, 35(22), e7724.
    15. Ramaiah, M., Chithanuru, V., Padma, A., & Ravi, V. (2022). A review of security vulnerabilities in industry 4.0 application and the possible solutions using blockchain. Cyber Security Applications for Industry 4.0, 63-95.
    16. Padma, A., Chithanuru, V., Uppamma, P., & VishnuKumar, R. (2024). Exploring Explainable AI in Healthcare: Challenges and Future Directions. In Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry (pp. 199-233). IGI Global.
    17. Prashanth, J. S., & Nandury, S. V. (2015, June). Cluster-based rendezvous points selection for reducing tour length of mobile element in WSN. In 2015 IEEE International Advance Computing Conference (IACC) (pp. 1230-1235). IEEE.
    18. Prashanth, J. S., & Nandury, S. V. (2019). A Cluster—based Approach for Minimizing Energy Consumption by Reducing Travel Time of Mobile Element in WSN. International Journal of Computers Communications & Control, 14(6), 691-709.
    19. Kumar, K. A., Pabboju, S., & Desai, N. M. S. (2014). Advance text steganography algorithms: an overview. International Journal of Research and Applications, 1(1), 31-35.
    20. Shyam, D. N. M., & Hussain, M. A. (2023). Mutual authenticated key agreement in Wireless Infrastructure-less network by Chaotic Maps based Diffie-Helman Property. Fusion: Practice & Applications, 13(2).
    21. Shyam, D. N. M., & Hussain, M. A. (2023). A Naive Bayes-Driven Mechanism for Mitigating Packet-Dropping Attacks in Autonomous Wireless Networks. Ingenierie des Systemes d’Information, 28(4), 1019.
    22. Hnamte, V., & Balram, G. (2022). Implementation of Naive Bayes Classifier for Reducing DDoS Attacks in IoT Networks. Journal of Algebraic Statistics, 13(2), 2749-2757.
    23. Balram, G., Anitha, S., & Deshmukh, A. (2020, December). Utilization of renewable energy sources in generation and distribution optimization. In IOP Conference Series: Materials Science and Engineering (Vol. 981, No. 4, p. 042054). IOP Publishing.
    24. Subrahmanyam, V., Sagar, M., Balram, G., Ramana, J. V., Tejaswi, S., & Mohammad, H. P. (2024, May). An Efficient Reliable Data Communication For Unmanned Air Vehicles (UAV) Enabled Industry Internet of Things (IIoT). In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) (pp. 1-4). IEEE.
    25. Balram, G., Poornachandrarao, N., Ganesh, D., Nagesh, B., Basi, R. A., & Kumar, M. S. (2024, September). Application of Machine Learning Techniques for Heavy Rainfall Prediction using Satellite Data. In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC) (pp. 1081-1087). IEEE.
    26. Balram, G., & Kumar, K. K. (2022). Crop field monitoring and disease detection of plants in smart agriculture using internet of things. International Journal of Advanced Computer Science and Applications, 13(7).
    27. Mahammad, F. S., Viswanatham, V. M., Tahseen, A., Devi, M. S., & Kumar, M. A. (2024, July). Key distribution scheme for preventing key reinstallation attack in wireless networks. In AIP Conference Proceedings (Vol. 3028, No. 1). AIP Publishing.
    28. Tahseen, A., Shailaja, S. R., & Ashwini, Y. (2024). Extraction for Big Data Cyber Security Analytics. Advances in Computational Intelligence and Informatics: Proceedings of ICACII 2023, 993, 365.
    29. Tahseen, A., Shailaja, S. R., & Ashwini, Y. (2023, December). Security-Aware Information Classification Using Attributes Extraction for Big Data Cyber Security Analytics. In International Conference on Advances in Computational Intelligence and Informatics (pp. 365-373). Singapore: Springer Nature Singapore.
    30. Lavanya, P. (2024). Personalized Medicine Recommendation System Using Machine Learning.
    31. Lavanya, P. (2024). In-Cab Smart Guidance and support system for Dragline operator.
    32. Lavanya, P. (2024). Price Comparison of GeM Products with other eMarketplaces.
    33. Kovoor, M., Durairaj, M., Karyakarte, M. S., Hussain, M. Z., Ashraf, M., & Maguluri, L. P. (2024). Sensor-enhanced wearables and automated analytics for injury prevention in sports. Measurement: Sensors, 32, 101054.
    34. Rao, N. R., Kovoor, M., Kishor Kumar, G. N., & Parameswari, D. V. L. (2023). Security and privacy in smart farming: challenges and opportunities. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7).
    35. Madhuri, K. (2023). Security Threats and Detection Mechanisms in Machine Learning. Handbook of Artificial Intelligence, 255.
    36. Madhuri, K., Viswanath, N. K., & Gayatri, P. U. (2016, November). Performance evaluation of AODV under Black hole attack in MANET using NS2. In 2016 international conference on ICT in Business Industry & Government (ICTBIG) (pp. 1-3). IEEE.
    37. Madhuri, K. (2022). A New Level Intrusion Detection System for Node Level Drop Attacks in Wireless Sensor Network. Journal of Algebraic Statistics, 13(1), 159-168.
    38. Reddy, P. R. S., Bhoga, U., Reddy, A. M., & Rao, P. R. (2017). OER: Open Educational Resources for Effective Content Management and Delivery. Journal of Engineering Education Transformations, 30(3), 322-326.
    39. Reddy, P. R. S., & Ravindranath, K. (2024). Enhancing Secure and Reliable Data Transfer through Robust Integrity. Journal of Electrical Systems, 20, 900-910.
    40. REDDY, P. R. S., & RAVINDRANATH, K. (2022). A HYBRID VERIFIED RE-ENCRYPTION INVOLVED PROXY SERVER TO ORGANIZE THE GROUP DYNAMICS: SHARING AND REVOCATION. Journal of Theoretical and Applied Information Technology, 100(13).
    41. Reddy, B. A., & Reddy, P. R. S. (2012). Effective data distribution techniques for multi-cloud storage in cloud computing. CSE, Anurag Group of Institutions, Hyderabad, AP, India.
    42. Srilatha, P., Murthy, G. V., & Reddy, P. R. S. (2020). Integration of Assessment and Learning Platform in a Traditional Class Room Based Programming Course. Journal of Engineering Education Transformations, 33, 179-184.
    43. Raj, R. S., & Raju, G. P. (2014, December). An approach for optimization of resource management in Hadoop. In International Conference on Computing and Communication Technologies (pp. 1-5). IEEE.
    44. Reddy, P. R. S., Bhoga, U., Reddy, A. M., & Rao, P. R. (2017). OER: Open Educational Resources for Effective Content Management and Delivery. Journal of Engineering Education Transformations, 30(3), 322-326.
    45. Ramana, A. V., Bhoga, U., Dhulipalla, R. K., Kiran, A., Chary, B. D., & Reddy, P. C. S. (2023, June). Abnormal Behavior Prediction in Elderly Persons Using Deep Learning. In 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) (pp. 1-5). IEEE.
    46. Ujwala, B., & Reddy, P. R. S. (2016). An effective mechanism for integrity of data sanitization process in the cloud. European Journal of Advances in Engineering and Technology, 3(8), 82-84.
    47. DASTAGIRAIAH, D. (2024). A System for Analysing call drop dynamics in the telecom industry using Machine Learning and Feature Selection. Journal of Theoretical and Applied Information Technology, 102(22).
    48. Sudhakar, R. V., Dastagiraiah, C., Pattem, S., & Bhukya, S. (2024). Multi-Objective Reinforcement Learning Based Algorithm for Dynamic Workflow Scheduling in Cloud Computing. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 12(3), 640-649.
    49. PushpaRani, K., Roja, G., Anusha, R., Dastagiraiah, C., Srilatha, B., & Manjusha, B. (2024, June). Geological Information Extraction from Satellite Imagery Using Deep Learning. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
    50. Latha, S. B., Dastagiraiah, C., Kiran, A., Asif, S., Elangovan, D., & Reddy, P. C. S. (2023, August). An Adaptive Machine Learning model for Walmart sales prediction. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 988-992). IEEE.
    51. Rani, K. P., Reddy, Y. S., Sreedevi, P., Dastagiraiah, C., Shekar, K., & Rao, K. S. (2024, June). Tracking The Impact of PM Poshan on Child’s Nutritional Status. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-4). IEEE.
    52. Selvaprasanth, P., Karthick, R., Meenalochini, P., & Prabaharan, A. M. (2025). FPGA implementation of hybrid Namib beetle and battle royale optimization algorithm fostered linear phase finite impulse response filter design. Analog Integrated Circuits and Signal Processing, 123(2), 33.
    53. Deepa, R., Karthick, R., & Senthilkumar, R. (2025). Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm. Computer Standards & Interfaces, 92, 103934.
    54. Kumar, T. V., Karthick, R., Nandhini, C., Annalakshmi, M., & Kanna, R. R. (2025). 20 GaN Power HEMT-Based Amplifiers. Circuit Design for Modern Applications, 320.
    55. Velayudham, A., Karthick, R., Sivabalan, A., & Sathya, V. (2025). IoT enabled smart healthcare system for COVID-19 classification using optimized robust spatiotemporal graph convolutional networks. Biomedical Signal Processing and Control, 100, 107104.
    56. Gayathri, P., Balamurugan, J., Gowthami, M., Usha, R., Karthick, R., & Selvan, R. S. (2025). Factors Influencing Customers’ Inclination to buy Green Products: An Indian Perspective. In Elevating Brand Loyalty With Optimized Marketing Analytics and AI (pp. 185-202). IGI Global Scientific Publishing.
    57. Ramkumar, G., Bhuvaneswari, J., Venugopal, S., Kumar, S., Ramasamy, C. K., & Karthick, R. (2025). Enhancing customer segmentation: RFM analysis and K-Means clustering implementation. In Hybrid and Advanced Technologies (pp. 70-76). CRC Press.
    58. Tamilselvi, M., Kalaivani, S. S. S., Sunderasan, V., Sailaja, K., Gopal, D., & Karthick, R. (2025). Deep learning for object detection and identification. In Hybrid and Advanced Technologies (pp. 218-223). CRC Press.
    1. Sidharth, S. (2022). Zero Trust Architecture: A Key Component of Modern Cybersecurity Frameworks.
    2. Sidharth, S. (2018). Optimized Cooling Solutions for Hybrid Electric Vehicle Powertrains.
    1. Kumar, T. V. (2024). A Comprehensive Empirical Study Determining Practitioners’ Views on Docker Development Difficulties: Stack Overflow Analysis.
    2. Kumar, T. V. (2024). A New Framework and Performance Assessment Method for Distributed Deep Neural NetworkBased Middleware for Cyberattack Detection in the Smart IoT Ecosystem.
    3. Turlapati, V. R., Thirunavukkarasu, T., Aiswarya, G., Thoti, K. K., Swaroop, K. R., & Mythily, R. (2024, November). The Impact of Influencer Marketing on Consumer Purchasing Decisions in the Digital Age Based on Prophet ARIMA-LSTM Model. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-6). IEEE.
    4. Raju, P., Arun, R., Turlapati, V. R., Veeran, L., & Rajesh, S. (2024). Next-Generation Management on Exploring AI-Driven Decision Support in Business. In Optimizing Intelligent Systems for Cross-Industry Application (pp. 61-78). IGI Global.
    1. Sreekanthaswamy, N., Anitha, S., Singh, A., Jayadeva, S. M., Gupta, S., Manjunath, T. C., & Selvakumar, P. (2025). Digital Tools and Methods. Enhancing School Counseling With Technology and Case Studies, 25.
    1. Sreekanthaswamy, N., & Hubballi, R. B. (2024). Innovative Approaches To Fmcg Customer Journey Mapping: The Role Of Block Chain And Artificial Intelligence In Analyzing Consumer Behavior And Decision-Making. Library of Progress-Library Science, Information Technology & Computer, 44(3).Deshmukh, M. C., Ghadle, K. P., & Jadhav, O. S. (2020). Optimal solution of fully fuzzy LPP with symmetric HFNs. In Computing in Engineering and Technology: Proceedings of ICCET 2019 (pp. 387-395). Springer Singapore.
    2. Chinchodkar, K. N., & Jadhav, O. S. (2017). Development of mathematical model for the solid waste management on dumping ground at Mumbai for the reduction of existence cost. Int. J. Statist. Syst, 12, 145-155.
    3. Kalluri, V. S. Optimizing Supply Chain Management in Boiler Manufacturing through AI-enhanced CRM and ERP Integration. International Journal of Innovative Science and Research Technology (IJISRT).
    4. Kalluri, V. S. Impact of AI-Driven CRM on Customer Relationship Management and Business Growth in the Manufacturing Sector. International Journal of Innovative Science and Research Technology (IJISRT).
    5. Kalluri, S. V. S., & Narra, S. (2024). Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing. vol, 9, 6.
    6. Al-Ghanimi, M. G., Hanif, O., Jain, M. V., Kumar, A. S., Rao, R., Kavin, R., … & Hossain, M. A. (2022, December). Two TS-Fuzzy Controllers based Direct Torque Control of 5-Phase Induction Motor. In 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (pp. 1-6). IEEE.
    7. Sameera, K., & MVR, S. A. R. (2014). Improved power factor and reduction of harmonics by using dual boost converter for PMBLDC motor drive. Int J Electr Electron Eng Res, 4(5), 43-51.
    8. Srinivasu, B., Prasad, P. V. N., & Rao, M. R. (2006, December). Adaptive controller design for permanent magnet linear synchronous motor control system. In 2006 International Conference on Power Electronic, Drives and Energy Systems (pp. 1-6). IEEE.
    9. Rao, M. R., & Prasad, P. V. N. (2014). Modelling and Implementation of Sliding Mode Controller for PMBDC Motor Drive. International journal of advanced research in electrical, electronics and instrumentation engineering, 3(6).
    1. Sidharth, S. (2017). Real-Time Malware Detection Using Machine Learning Algorithms.
    2. Sidharth, S. (2017). Access Control Frameworks for Secure Hybrid Cloud Deployments.
    1. Kumar, T. V. (2024). Developments and Uses of Generative Artificial Intelligence and Present Experimental Data on the Impact on Productivity Applying Artificial Intelligence that is Generative.
    2. Kumar, T. V. (2024). A Comparison of SQL and NO-SQL Database Management Systems for Unstructured Data.
    3. Jadhav, V. S., & Jadhav, O. S. (2019). Solving flow-shop scheduling problem to minimize total elapsed time using fuzzy approach. International Journal of Statistics and Applied Mathematics, 4(5), 130-133.
    4. Deshmukh, M., Ghadle, K., & Jadhav, O. (2020). An innovative approach for ranking hexagonal fuzzy numbers to solve linear programming problems. International Journal on Emerging Technologies, 11(2), 385-388.
    1. Sidharth, S. (2016). Establishing Ethical and Accountability Frameworks for Responsible AI Systems.
    1. Sidharth, S. (2015). AI-Driven Detection and Mitigation of Misinformation Spread in Generated Content.
    2. Sharma, S., & Dutta, N. (2024). Examining ChatGPT’s and Other Models’ Potential to Improve the Security Environment using Generative AI for Cybersecurity.
    3. Tambi, V. K., & Singh, N. (2015). Potential Evaluation of REST Web Service Descriptions for Graph-Based Service Discovery with a Hypermedia Focus.
    4. Patil, R. D., & Jadhav, O. S. (2016). Some contribution of statistical techniques in big data: a review. International Journal on Recent and Innovation Trends in Computing and Communication, 4(4), 293-303.
    5. Jadhava, V. S., Buktareb, S. U., & Jadhavc, O. S. (2024). Ranking of Octagonal Fuzzy Numbers for Solving Fuzzy Job Sequencing Problem Using Robust Ranking Technique. Journal of Statistics, Optimization and Data Science, 1(2), 22-28.
    1. Sidharth, S. (2015). Privacy-Preserving Generative AI for Secure Healthcare Synthetic Data Generation.
    1. Sidharth, S. (2018). Post-Quantum Cryptography: Readying Security for the Quantum Computing Revolution.
    2. Tambi, V. K., & Singh, N. (2019). Development of a Project Risk Management System based on Industry 4.0 Technology and its Practical Implications. Development, 7(11).
    3. Chaudhari, S. A., Gawali, B. W., & Jadhav, O. S. (2022). Statistical analysis of EEG data for attention deficit hyperactivity disorder. Journal of Positive School Psychology, 4046-4053.
    4. Jadhav, S., Machale, A., Mharnur, P., Munot, P., & Math, S. (2019, September). Text based stress detection techniques analysis using social media. In 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA) (pp. 1-5). IEEE.
    5. Thepade, D. S., Mandal, P. R., & Jadhav, S. (2015). Performance Comparison of Novel Iris Recognition Techniques Using Partial Energies of Transformed Iris Images and Enegy CompactionWith Hybrid Wavelet Transforms. In Annual IEEE India Conference (INDICON).
    6. Kiran, A., Sonker, A., Jadhav, S., Jadhav, M. M., Naga Ramesh, J. V., & Muniyandy, E. (2024). Secure Communications with THz Reconfigurable Intelligent Surfaces and Deep Learning in 6G Systems. Wireless Personal Communications, 1-17.
    7. Anitha, C., Tellur, A., Rao, K. B., Kumbhar, V., Gopi, T., Jadhav, S., & Vidhya, R. G. (2024). Enhancing Cyber-Physical Systems Dependability through Integrated CPS-IoT Monitoring. International Research Journal of Multidisciplinary Scope, 5(2), 706-713.
    8. Vandana, C. P., Basha, S. A., Madiajagan, M., Jadhav, S., Matheen, M. A., & Maguluri, L. P. (2024). IoT resource discovery based on multi faected attribute enriched CoAP: smart office seating discovery. Wireless Personal Communications, 1-18.
    9. Jadhav, S., Durairaj, M., Reenadevi, R., Subbulakshmi, R., Gupta, V., & Ramesh, J. V. N. (2024). Spatiotemporal data fusion and deep learning for remote sensing-based sustainable urban planning. International Journal of System Assurance Engineering and Management, 1-9.
    10. Jadhav, S., Chaudhari, V., Barhate, P., Deshmukh, K., & Agrawal, T. (2021). Extreme Gradient Boosting for Predicting Stock Price Direction in Context of Indian Equity Markets. In Intelligent Sustainable Systems: Selected Papers of WorldS4 2021, Volume 2 (pp. 321-330). Singapore: Springer Nature Singapore.
    11. Jadhav, S., Chaudhari, V., Barhate, P., Deshmukh, K., & Agrawal, T. (2021). REVIEW PAPER ON: ALGORITHMIC TRADING USING ARTIFICIAL INTELLEGENCE.
    12. Thamma, S. R. T. S. R. (2024). Optimization of Generative AI Costs in Multi-Agent and Multi-Cloud Systems.
    13. Alsudairy, M. A. T., & Vasista, T. G. K. (2014, May). CRASP—a strategic methodology perspective for sustainable value chain management. In Proceedings of the 23rd IBIMA Conference.
    14. Vasista, T. G. K., & AlAbdullatif, A. M. (2015). Turning customer insights contributing to VMI based decision support system in demand Chain management. International Journal of Managing Value and Supply Chains, 6(2), 37-45.
    15. AlSudairi, M., & Vasista, T. G. K. (2012, September). Service design systems driven innovation approach for total innovation management. In Proceedings of the 7th European Conference on Innovation and Entrepreneurship: ECIE (p. 8). Academic Conferences Limited.
    16. Vasista, T. G. K. (2007). Wise CRM engine. Synergy-The Journal of Marketing, 5(1), 123-127.
    17. Vasista, T. G. K. (2016). Thoughtful approaches to implementation of electronic rulemaking. Int. J. Manag. Pub. Sect. Inf. Commun. Technol, 7(2), 43-53.
    18. Vasista, T. G. K. (2015). Strategic Business Challenges in Cloud Systems. Int. J. Cloud Comput. Serv. Archit., 5(4), 1-3.
    19. Vasista, T. G. K. (2013). System, spiritual and philosophical perspectives of human life and the role of governance in a socio-economic setting. Unpublished Paper Developed at King Saud University, Riyadh, KSA.
    20. Thamma, S. R. T. S. R. (2024). Revolutionizing Healthcare: Spatial Computing Meets Generative AI.
    21. Kalaiselvi, B., & Thangamani, M. (2020). An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques. Measurement, 162, 107885.
    22. Prabhu Kavin, B., Karki, S., Hemalatha, S., Singh, D., Vijayalakshmi, R., Thangamani, M., … & Adigo, A. G. (2022). Machine learning‐based secure data acquisition for fake accounts detection in future mobile communication networks. Wireless Communications and Mobile Computing, 2022(1), 6356152.
    23. Geeitha, S., & Thangamani, M. (2018). Incorporating EBO-HSIC with SVM for gene selection associated with cervical cancer classification. Journal of medical systems, 42(11), 225.
    24. Thangamani, M., & Thangaraj, P. (2010). Integrated Clustering and Feature Selection Scheme for Text Documents. Journal of Computer Science, 6(5), 536.
    25. Gangadhar, C., Chanthirasekaran, K., Chandra, K. R., Sharma, A., Thangamani, M., & Kumar, P. S. (2022). An energy efficient NOMA-based spectrum sharing techniques for cell-free massive MIMO. International Journal of Engineering Systems Modelling and Simulation, 13(4), 284-288.
    26. Narmatha, C., Thangamani, M., & Ibrahim, S. J. A. (2020). Research scenario of medical data mining using fuzzy and graph theory. International Journal of Advanced Trends in Computer Science and Engineering, 9(1), 349-355.
    27. Thangamani, M., & Thangaraj, P. (2013). Fuzzy ontology for distributed document clustering based on genetic algorithm. Applied Mathematics & Information Sciences, 7(4), 1563-1574.
    28. Surendiran, R., Aarthi, R., Thangamani, M., Sugavanam, S., & Sarumathy, R. (2022). A Systematic Review Using Machine Learning Algorithms for Predicting Preterm Birth. International Journal of Engineering Trends and Technology, 70(5), 46-59.
    29. Thangamani, M., & Thangaraj, P. (2010). Ontology based fuzzy document clustering scheme. Modern Applied Science, 4(7), 148.
    30. Ibrahim, S. J. A., & Thangamani, M. (2018, November). Momentous Innovations in the prospective method of Drug development. In Proceedings of the 2018 International Conference on Digital Medicine and Image Processing (pp. 37-41).
    31. Thamma, S. R. (2024). Cardiovascular image analysis: AI can analyze heart images to assess cardiovascular health and identify potential risks.
    32. Arun, A., Ali A. Alalmai, and D. Gunaseelan. “Operational Need and Importance of Capacity Management into Hotel Industry–A Review.” (2020).
    33. Gunaseelan, D., & Kumar, G. R. (2024). An umbrella view on food habits in the context of health and sustainability for sports persons. Salud, Ciencia y Tecnología-Serie de Conferencias, (3), 890.
    34. Gunaseelan, D., & Arun, A. Tourist Destination Satisfaction: Analysis of Kanyakumari the Spot with Scenic Beauty and Spiritual Temples. Emperor Journal of Economics and Social Science Research, 3(1).
    35. Thamma, S. R. T. S. R. (2024). Generative AI in Graph-Based Spatial Computing: Techniques and Use Cases.
    36. Kumar, J. S., Archana, B., Muralidharan, K., & Kumar, V. S. (2025). Graph Theory: Modelling and Analyzing Complex System. Metallurgical and Materials Engineering, 31(3), 70-77.
    37. Kumar, J. S., Archana, B., Muralidharan, K., & Srija, R. (2025). Spectral Graph Theory: Eigen Values Laplacians and Graph Connectivity. Metallurgical and Materials Engineering, 31(3), 78-84.
    38. Srija, R., Kumar, J. S., & Muralidharan, K. (2025). An improvement in estimating the population mean by using quartiles and correlation coefficient. Mathematics in Engineering, Science & Aerospace (MESA), 16(1).
    39. Kumar, J. S., Murthy, S., Kumar, B. R., & Solaiappam, S. (2017, January). p-Value analyze the set of optimal value in MOFTP. In 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 1-5). IEEE.
    40. Anandasubramanian, C. P., & Selvaraj, J. (2024). NAVIGATING BANKING LIQUIDITY-FACTORS, CHALLENGES, AND STRATEGIES IN CORPORATE LOAN PORTFOLIOS. Tec Empresarial, 6(1).
    41. Madem, S., Katuri, P. K., Kalra, A., & Singh, P. (2023, May). System Design for Financial and Economic Monitoring Using Big Data Clustering. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
    42. Srikanth, V., & Dhanapal, D. R. (2012). E-commerce online security and trust marks. International Journal of Computer Engineering and Technology, 3(2), 238-255.
    43. Lopez, S., Sarada, V., Praveen, R. V. S., Pandey, A., Khuntia, M., & Haralayya, D. B. (2024). Artificial intelligence challenges and role for sustainable education in india: Problems and prospects. Sandeep Lopez, Vani Sarada, RVS Praveen, Anita Pandey, Monalisa Khuntia, Bhadrappa Haralayya (2024) Artificial Intelligence Challenges and Role for Sustainable Education in India: Problems and Prospects. Library Progress International, 44(3), 18261-18271.
    44. Yamuna, V., Praveen, R. V. S., Sathya, R., Dhivva, M., Lidiya, R., & Sowmiya, P. (2024, October). Integrating AI for Improved Brain Tumor Detection and Classification. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1603-1609). IEEE.
    45. Kumar, N., Kurkute, S. L., Kalpana, V., Karuppannan, A., Praveen, R. V. S., & Mishra, S. (2024, August). Modelling and Evaluation of Li-ion Battery Performance Based on the Electric Vehicle Tiled Tests using Kalman Filter-GBDT Approach. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.
    46. Sharma, S., Vij, S., Praveen, R. V. S., Srinivasan, S., Yadav, D. K., & VS, R. K. (2024, October). Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1631-1636). IEEE.
    47. Anuprathibha, T., Praveen, R. V. S., Sukumar, P., Suganthi, G., & Ravichandran, T. (2024, October). Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings. In 2024 Global Conference on Communications and Information Technologies (GCCIT) (pp. 1-5). IEEE.
    48. Shinkar, A. R., Joshi, D., Praveen, R. V. S., Rajesh, Y., & Singh, D. (2024, December). Intelligent solar energy harvesting and management in IoT nodes using deep self-organizing maps. In 2024 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1-6). IEEE.
    49. Praveen, R. V. S., Hemavathi, U., Sathya, R., Siddiq, A. A., Sanjay, M. G., & Gowdish, S. (2024, October). AI Powered Plant Identification and Plant Disease Classification System. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1610-1616). IEEE.
    50. Dhivya, R., Sagili, S. R., Praveen, R. V. S., VamsiLala, P. N. V., Sangeetha, A., & Suchithra, B. (2024, December). Predictive Modelling of Osteoporosis using Machine Learning Algorithms. In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 997-1002). IEEE.
    51. Kemmannu, P. K., Praveen, R. V. S., Saravanan, B., Amshavalli, M., & Banupriya, V. (2024, December). Enhancing Sustainable Agriculture Through Smart Architecture: An Adaptive Neuro-Fuzzy Inference System with XGBoost Model. In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA) (pp. 724-730). IEEE.

    Praveen, R. V. S. (2024). Data Engineering for Modern Applications. Addition Publishing House.

Abstract

In today’s digital world, online transaction security is essential to protect sensitive information. This project introduces a Secure Online Transaction System developed using Java and MySQL, with AES (Advanced Encryption Standard) for encryption. The system aims to prevent unauthorized access, data leaks, and fraud by encrypting transaction data during transfer and storage.Online Secure Transaction Cryptography

Using a client-server model, the client initiates transactions while the server processes them and interacts with the database. Java ensures cross-platform compatibility, and AES provides fast and reliable encryption with 128-bit blocks and variable key lengths (128, 192, or 256 bits). The system securely generates and manages encryption keys internally.

MySQL stores user credentials, transaction details, and logs with protection against SQL injection and unauthorized access. Overall, the project delivers a scalable and secure solution for digital payments. It demonstrates how cryptographic algorithms like AES, combined with Java and MySQL, can strengthen online transaction security.

Introduction

With the rapid growth of digital payments, securing online financial transactions has become more critical than ever. The Secure Online Transaction System aims to offer a reliable, encrypted environment for executing financial operations with enhanced data privacy and security. Built using Java for backend development and MySQL for data storage, the system incorporates AES encryption to safeguard sensitive transaction information from cyber threats and unauthorized access.Online Secure Transaction Cryptography

AES is a well-established encryption standard that provides strong data protection by encrypting transaction data during both storage and transmission. This significantly reduces the risk of data leaks, hacking attempts, and fraud. The system’s client-server model ensures structured communication between users and the backend, while the use of Java ensures cross-platform compatibility and scalability.

By combining these technologies, the system provides a seamless and secure user experience. It is suitable for deployment by individuals, enterprises, and financial institutions that seek to enhance their cybersecurity posture and ensure the trustworthiness of digital transactions. This project demonstrates how cryptographic techniques like AES can be effectively integrated into transaction systems to support safe and efficient online operations.

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