Enhancing the security of web applications through innovative patterns of integration of artificial intelligence
DOI:
https://doi.org/10.30837/ITSSI.2024.27.067Keywords:
Artificial Intelligence; Web Application Security; Financial Transactions; Machine Learning; Data Analysis; Fraud Detection; scikit-learn.Abstract
Ensuring the security of digital operations, especially in the areas of e-commerce and financial transactions, remains increasingly relevant. Therefore, the subject of research is the development of a specialized software library. This library aims to improve the security of web applications. The purpose of this study is to develop a software library that uses artificial intelligence and machine learning methods to analyze and improve the level of security of financial transactions. The use of these advanced technologies helps automate the detection of potentially fraudulent or risky transactions, thereby providing a higher level of user protection. The following tasks are solved in the article: analysis of modern methods of processing financial transactions and identification of possible security threats; development of a UML diagram of library classes for processing and analyzing financial transactions; testing and validation of the developed artificial intelligence model for assessing the security of financial transactions on real financial data. Machine learning methods were defined and applied using the scikit-learn library in Python, the algorithms of which are capable of analyzing large volumes of data and identifying potential risks with high accuracy. This ensures effective integration of artificial intelligence technologies. The following results were obtained in the work: the criteria for assessing the riskiness of financial transactions for the identification of potential risks are defined; the program operation algorithm is described, which includes procedures for determining and classifying transaction risks; pseudocode is presented, which illustrates the structure of classes and methods of the model, opening opportunities for its adaptation and scaling; methods of generating test data reproducing realistic scenarios of financial transactions have been developed; an analysis of the results was carried out to assess the effectiveness of the developed model. In conclusion, the results of research and testing allow us to evaluate the model's response to various data and its effectiveness in real conditions, as the work presents examples of processing various types of transactions. In addition, the study presents not only the development and validation of the developed model, but also the prospects of its use on a larger scale, integration with existing web applications.
References
Список літератури
Attkan A., Ranga V. Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security. Complex & Intelligent Systems. 2022. Vol. 8. P. 3559–3591. DOI: https://doi.org/10.1007/s40747-022-00667-z
Sobchuk V., Zamrii I., Laptiev S. Ensuring Functional Stability of Technological Processes as Cyberphysical Systems Using Neural Networks. Lecture Notes in Networks and Systems. 2023. Vol. 536. P. 581–592. DOI: https://doi.org/10.1007/978-3-031-20141-7_53
Latif S., Xian Wen F., Iwendi C., Wang L.-l., Mohsin S., Han Z., Band S. AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systems. Computer Communications. 2022. Vol. 181. P. 274–283. DOI: https://doi.org/10.1016/j.comcom.2021.09.029
Bonfanti M. Artificial intelligence and the offense–defense balance in cyber security. Cyber Security Politics; Socio-Technological Transformations and Political Fragmentation. 2022. 1st Edition. P. 64–77. DOI: https://doi.org/10.4324/9781003110224-6
Naik B., Mehta A., Yagnik H., Shah M. The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review. Complex & Intelligent Systems. 2021. Vol. 8. P. 1763–1780. DOI: https://doi.org/10.1007/s40747-021-00494-8
Abdullahi M., Baashar Y., Alhussian H., Alwadain A., Aziz N., Capretz L., Abdulkadir S. Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review. Electronics. 2022. 11(2), 198. P. 2–27. DOI: https://doi.org/10.3390/electronics11020198
Ahanger T., Aljumah A., Atiquzzaman M. State-of-the-art survey of artificial intelligent techniques for IoT security. Computer Networks. 2022. Vol. 206. 108771 р. DOI: https://doi.org/10.1016/j.comnet.2022.108771
Ramasamy L., Khan F., Shah M., Prasad B., Iwendi C., Biamba C. Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring. Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence. 2022. 22(3), 1076. P. 2–16. DOI: https://doi.org/10.3390/s22031076
Ghillani D. Deep Learning and Artificial Intelligence Framework to Improve the Cyber Security. American Journal of Artificial Intelligence. 2022. 11 p. DOI: https://doi.org/10.22541/au.166379475.54266021/v1
Pise A., Almuzaini K., Ahanger T., Farouk A., Pant K., Pareek P., Nuagah S. Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues. Computational Intelligence and Neuroscience. 2022. Vol. 2022, Article ID 8421434, 14 p. DOI: https://doi.org/10.1155/2022/8421434
Zhang Z., Al Hamadi H., Damiani E., Yeun C. Y., Taher F. Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research. IEEE Access. 2022. Vol. 10, P. 93104–93139. DOI: https://doi.org/10.1109/ACCESS.2022.3204051
Gill S., Xu M., Ottaviani C., Patros P., Bahsoon R., Shaghaghi A., Golec M., Stankovski V., Wu H., Abraham A., Singh M., Mehta H., Ghosh S., Baker T., Parlikad A., Lutfiyya H., Kanhere S., Sakellariou R., Dustdar S., Rana O., Uhlig S. AI for next generation computing: Emerging trends and future directions. Internet of Things. 2022. Vol. 19. 100514 р. DOI: https://doi.org/10.1016/j.iot.2022.100514
Kumar S., Lim W., Sivarajah U., Kaur J. Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis. Information Systems Frontiers. 2023. Vol. 25. P. 871–896. DOI: https://doi.org/10.1007/s10796-022-10279-0
Yathiraju N. Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System. International Journal of Electrical, Electronics and Computers. 2022. Vol. 7, Issue 2. P. 1–26. DOI: http://dx.doi.org/10.22161/eec.72.1
Sujith A., Sajja G., Mahalakshmi V., Nuhmani S., Prasanalakshmi B. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neuroscience Informatics. 2022. Vol. 2, Issue 3. 100028 р. DOI: https://doi.org/10.1016/j.neuri.2021.100028
Nasim S., Ali M., Kulsoom U. Artificial intelligence incidents & ethics: a narrative review. Computer Science and Information Technology. 2022. Vol. 2, No 2. P. 52–64. DOI: http://dx.doi.org/10.54489/ijtim.v2i2.80
Kunduru A. Artificial intelligence advantages in cloud fintech application security. Central asian journal of mathematical theory and computer sciences. 2023. Vol. 4, No. 8. P. 48–53 URL: https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/492
Chang V., Bhavani V., Xu A., Hossain M. An artificial intelligence model for heart disease detection using machine learning algorithms. Healthcare Analytics. 2022. Vol. 2. 100016 р. DOI: https://doi.org/10.1016/j.health.2022.100016
Babitha M., Sushama C., Gudivada V., Kazi K., Bandaru S. Trends of Artificial Intelligence for Online Exams in Education. International Journal of Early Childhood Special Education. 2022. 14(01). P. 2457–2463. URL: https://www.researchgate.net/publication/360513613_Trends_of_Artificial_Intelligence_for_Online_Exams_in_Education
Esenogho E., Djouani K., Kurien A. M. Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect. IEEE Access. 2022. Vol. 10. P. 4794–4831. DOI: https://doi.org/10.1109/ACCESS.2022.3140595
Bi S., Wang C., Zhang J., Huang W., Wu B., Gong Y., Ni W. A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries. AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems. 2022. No. 8. 2991 р. DOI: https://doi.org/10.3390/s22082991
References
Attkan, A., Ranga, V. (2022), "Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security", Complex & Intelligent Systems, Vol. 8, P. 3559–3591. DOI: https://doi.org/10.1007/s40747-022-00667-z
Sobchuk, V., Zamrii, I., Laptiev, S. (2023), "Ensuring Functional Stability of Technological Processes as Cyberphysical Systems Using Neural Networks", Lecture Notes in Networks and Systems, Vol. 536, P. 581–592. DOI: https://doi.org/10.1007/978-3-031-20141-7_53
Latif, S., Xian, Wen F., Iwendi, C., Wang, L.-l., Mohsin, S., Han, Z., Band, S. (2022), "AI-empowered, blockchain and SDN integrated security architecture for IoT network of cyber physical systems", Computer Communications, Vol. 181, P. 274–283. DOI: https://doi.org/10.1016/j.comcom.2021.09.029
Bonfanti, M. (2022), "Artificial intelligence and the offense–defense balance in cyber security", Cyber Security Politics; Socio-Technological Transformations and Political Fragmentation, 1st Edition, P. 64–77. DOI: https://doi.org/10.4324/9781003110224-6
Naik, B., Mehta, A., Yagnik, H., Shah, M. (2021), "The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review", Complex & Intelligent Systems, Vol. 8, P. 1763–1780. DOI: https://doi.org/10.1007/s40747-021-00494-8
Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L., Abdulkadir, S. (2022), "Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review", Electronics, 11(2), 198, P. 2–27. DOI: https://doi.org/10.3390/electronics11020198
Ahanger, T., Aljumah, A., Atiquzzaman, M. (2022), "State-of-the-art survey of artificial intelligent techniques for IoT security", Computer Networks, Vol. 206, 108771 р. DOI: https://doi.org/10.1016/j.comnet.2022.108771
Ramasamy, L., Khan, F., Shah, M., Prasad, B., Iwendi, C., Biamba, C. (2022), "Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring", Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence, 22(3), 1076. P. 2–16. DOI: https://doi.org/10.3390/s22031076
Ghillani, D. (2022), "Deep Learning and Artificial Intelligence Framework to Improve the Cyber Security", American Journal of Artificial Intelligence, 11 p. DOI: https://doi.org/10.22541/au.166379475.54266021/v1
Pise, A., Almuzaini, K., Ahanger, T., Farouk, A., Pant, K., Pareek, P., Nuagah, S. (2022), "Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues", Computational Intelligence and Neuroscience, Vol. 2022, Article ID 8421434, 14 p. DOI: https://doi.org/10.1155/2022/8421434
Zhang, Z., Al Hamadi, H., Damiani, E., Yeun, C. Y., Taher, F. (2022), "Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research", IEEE Access, Vol. 10, P. 93104–93139. DOI: https://doi.org/10.1109/ACCESS.2022.3204051
Gill, S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., Golec, M., Stankovski, V., Wu, H., Abraham, A., Singh, M., Mehta, H., Ghosh, S., Baker, T., Parlikad, A., Lutfiyya, H., Kanhere, S., Sakellariou, R., Dustdar, S., Rana, O., Uhlig, S. (2022), "AI for next generation computing: Emerging trends and future directions", Internet of Things, Vol. 19, 100514 р. DOI: https://doi.org/10.1016/j.iot.2022.100514
Kumar, S., Lim, W., Sivarajah, U., Kaur, J. (2023), "Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis", Information Systems Frontiers, Vol. 25, P. 871–896. DOI: https://doi.org/10.1007/s10796-022-10279-0
Yathiraju, N. (2022), "Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System", International Journal of Electrical, Electronics and Computers, Vol. 7, Issue 2, P. 1–26. DOI: http://dx.doi.org/10.22161/eec.72.1
Sujith, A., Sajja, G., Mahalakshmi, V., Nuhmani, S., Prasanalakshmi, B. (2022), "Systematic review of smart health monitoring using deep learning and Artificial intelligence", Neuroscience Informatics, Vol. 2, Issue 3, 100028 р. DOI: https://doi.org/10.1016/j.neuri.2021.100028
Nasim, S., Ali, M., Kulsoom, U. (2022), "Artificial intelligence incidents & ethics: a narrative review", Computer Science and Information Technology, Vol. 2, No 2, P. 52–64. DOI: http://dx.doi.org/10.54489/ijtim.v2i2.80
Kunduru, A. (2023), "Artificial intelligence advantages in cloud fintech application security", Central asian journal of mathematical theory and computer sciences, Vol. 4, No. 8, P. 48–53 URL: https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/492
Chang, V., Bhavani, V., Xu, A., Hossain, M. (2022), "An artificial intelligence model for heart disease detection using machine learning algorithms", Healthcare Analytics, Vol. 2, 100016 р. DOI: https://doi.org/10.1016/j.health.2022.100016
Babitha, M., Sushama, C., Gudivada, V., Kazi, K., Bandaru, S. (2022), "Trends of Artificial Intelligence for Online Exams in Education", International Journal of Early Childhood Special Education, 14(01), P. 2457–2463 URL: https://www.researchgate.net/publication/360513613_Trends_of_Artificial_Intelligence_for_Online_Exams_in_Education
Esenogho, E., Djouani, K., Kurien, A. (2022), "Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect", IEEE Access, Vol. 10, P. 4794–4831. DOI: https://doi.org/10.1109/ACCESS.2022.3140595
Bi, S., Wang, C., Zhang, J., Huang, W., Wu, B., Gong, Y., Ni, W. (2022), "A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries", AI-Aided Wireless Sensor Networks and Smart Cyber-Physical Systems, No. 8, 2991 р. DOI: https://doi.org/10.3390/s22082991
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
