ENHANCING SECURITY AUTOMATION IN ECOMMERCE PLATFORMS USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Keywords:
Machine Learning (ML), Artificial Intelligence (AI), ECommerce, Security Automation, Cyber Threats, Fraud, Detection, Anomaly Detection, User Behavior Analytics (UBA), Biometric Authentication, Phishing, Distributed, Denial Of Service (DDoS), Threat Intelligence, Adversarial AttacksAbstract
As eCommerce grows faster, increased insecurity has brought about the need for safeguarding measures since traditional methods cannot address modern-day technological cybersecurity attacks. This paper discusses the use of the Machine Learning Approach and Artificial Intelligence to address the challenges of security automation in e-commerce platforms. Incorporating these technologies can enhance threat identification, avoid fraud, and provide more efficient user identification methods in eCommerce businesses. Essential ML means that systems can go over large volumes of data to recognize patterns of malicious activity. At the same time, AI is self-acting regarding security threats, minimizing the time it takes for people’s involvement. The application of ML and AI spans several vital areas, including anomaly detection, fraud detection, and user behavior analytics, which in turn increases security automation and real-time threat mitigation. There are still some limitations, such as the requirement for vast sets of high-quality data for model training, the explainability of AI systems, and the susceptibility of the AI models to adversarial attacks. However, it is crucial to understand that the problems outlined above are still limitations of AI and ML implementations in eCommerce security automation, and the advantages are in terms of scalability, speed, and efficiency when it comes to threat detection. In recent years, advanced AI technology like Deep Reinforcement Learning (DRL), Federated Learning (FL), and Explainable AI (XAI) present new dimensions in e-commerce security in the future. These technologies are becoming more complex; hence, adopting AI and ML will be critical in security frameworks to protect customers’ trust and future business sustainability.
References
Abdulhafedh, A. (2022). Comparison between common statistical modeling techniques used in research, including: Discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest. Open Access Library Journal, 9(2), 1-19.
Abualkibash, M. M., Zaghouani, W., & Faris, H. (2020). “AI-based Anomaly Detection for Enhanced Security in Online Systems.” IEEE Access, 8, 189723-189734.
Adel, A. (2023). Unlocking the future: fostering human–machine collaboration and driving intelligent automation through industry 5.0 in smart cities. Smart Cities, 6(5), 2742-2782.
Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2015). A survey of outlier detection techniques in data mining. ACM Computing Surveys (CSUR), 38(1), 1-29.
Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.
Al-Ebrahim, M. A., Bunian, S., & Nour, A. A. (2023). Recent Machine-Learning-Driven Developments in E-Commerce: Current Challenges and Future Perspectives. Engineered Science, 28, 1044.
Alkhowaiter, W. (2021). "Account Takeover Fraud in E-Commerce: Trends and Prevention Measures." Journal of Information Security, 12(1), 45-56.
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8(3), 292.
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities, and challenges toward responsible AI. Information Fusion, 58, 82-115.
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.
Biggio, B., & Roli, F. (2018). "Wild patterns: Ten years after the rise of adversarial machine learning." Pattern Recognition, 84, 317-331.
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivkin, N., ... & van Overveldt, T. (2019). Towards federated learning at scale: System design. In Proceedings of the 2nd SysML Conference.
Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cybersecurity intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. ACM Computing Surveys (CSUR), 51(3), 1-36.
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023). A review of trustworthy and explainable artificial intelligence (xai). IEEe Access.
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58.
Chandrashekar, V. (2019). "The Role of AI in Threat Detection and Intelligence." International Journal of Information Security Science, 8(2), 50-59.
Chou, T. (2019). Cloud Security: A Comprehensive Guide to Secure Cloud Computing. Springer.
Das, A., Bonneau, J., Caesar, M., Borisov, N., & Wang, X. (2019). "The Tangled Web of Password Reuse." Communications of the ACM, 62(4), 74-81.
Digital Shadows. (2021). "The Rising Tide of Account Takeover Fraud." Digital Shadows Threat Report.
Diro, A. A., & Chilamkurti, N. (2018). "Distributed attack detection scheme using deep learning approach for Internet of Things." Future Generation Computer Systems, 82, 761-768.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Esquivel, S. C., & Burns, C. (2018). Reinforcement learning for cybersecurity: A review. In International Conference on Cyber Security (pp. 59-70).
Ferrag, M. A., Friha, O., Kantarci, B., Tihanyi, N., Cordeiro, L., Debbah, M., ... & Choo, K. K. R. (2023). Edge learning for 6G-enabled Internet of Things: A comprehensive survey of vulnerabilities, datasets, and defenses. IEEE Communications Surveys & Tutorials.
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems, and challenges. Computers & Security, 28(1-2), 18-28.
Gogoi, P., Bhattacharyya, D. K., Borah, B., & Kalita, J. K. (2011). A survey of outlier detection methods in network anomaly identification. The Computer Journal, 54(4), 570-588.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine, 38(3), 50-57.
Gu, J., Yu, F. R., Liu, J., & Tang, T. (2021). Reinforcement learning for edge caching and dynamic content distribution in Internet of Things (IoT) networks. IEEE Transactions on Vehicular Technology, 70(7), 6976-6990.
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaay7120.
Hinton, G. E. (2012). Neural networks for machine learning. Coursera Lecture Notes.
Hoque, N., Bhuyan, M. H., & Bhattacharyya, D. K. (2020). "Botnet in DDoS Attack Detection Using NLP Techniques." Journal of Cybersecurity, 12(4), 211-230.
Husák, M., Čermák, M., Jirsík, T., & Komárková, J. (2018). Network-based anomaly detection using machine learning. Security and Communication Networks, 2018.
Jain, A. K., & Kumar, A. (2019). Biometric recognition: An overview. Springer Handbook of Biometrics, 1-42.
Juniper Research. (2020). "Online Payment Fraud Losses to Exceed $20 Billion in 2020."
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1), 1-210.
Kantardzic, M. (2011). Data mining: Concepts, models, methods, and algorithms. John Wiley & Sons.
Kayacik, H. G., Zincir-Heywood, A. N., & Heywood, M. I. (2005). Selecting features for intrusion detection: A feature relevance analysis on KDD 99 intrusion detection datasets. Proceedings of the Third Annual Conference on Privacy, Security and Trust.
Kim, J., Kim, H., & Cho, J. (2020). User satisfaction with biometric systems in eCommerce: A study on fingerprint scanning. Journal of Information Security and Applications, 54, 102512.
Kumar, A., & Singh, P. K. (2021). A review of deep reinforcement learning for cybersecurity applications. IEEE Access, 9, 126245-126267.
Lee, W., & Stolfo, S. J. (2000). A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security (TISSEC), 3(4), 227-261.
Liang, X., & Zhao, J. (2020). "Towards Better Zero-Day Threat Detection." IEEE Transactions on Information Forensics and Security, 15, 1381-1392.
Liu, F., Huang, X., & Zhang, Y. (2020). Real-time anomaly detection in eCommerce using unsupervised learning. IEEE Transactions on Industrial Informatics, 16(8), 5435-5442.
Liu, Q., Yang, Y., Ding, M., Guo, W., Wang, Q., & Jin, S. (2022). Reinforcement learning and deep learning-based attacks on network intrusion detection systems. Journal of Network and Computer Applications, 210, 103512.
Liu, X., Zhang, X., & Wang, C. (2020). "DDoS Attacks on E-Commerce Systems: Vulnerabilities and Solutions." Journal of Cybersecurity, 8(2), 109-119.
Luo, X., Brody, R., Seazzu, A., & Burd, S. (2018). User behavior analytics: Applications and challenges in cybersecurity. Computers & Security, 74, 93-111.
Matthias, G., Gadepalli, N., & Jain, A. (2021). "AI for Instantaneous Response to Cybersecurity Incidents." Journal of Cybersecurity and Privacy, 5(2), 101-115.
McCray, K. L. (2023). Vulnerabilities and Threats in Mobile Banking that Financial Institutions Must Understand to Reduce Mobile Banking Fraud (Doctoral dissertation, Marymount University).
Mehdi, M., Shah, S. A., & Ahmed, J. (2021). "NLP-Based Dark Web Surveillance for Emerging Threat Detection." Cybersecurity Analytics and Operations, 3(1), 67-80.
Mitropoulos, F., Lourakis, M., & Christodoulou, E. (2020). "Real-Time Threat Intelligence Using AI-Based Cybersecurity Systems." Journal of Cybersecurity Technology, 4(3), 181-195.
Nguyen, K., Le, T., & Lee, W. (2020). "NLP Techniques in Detecting Fraudulent E-commerce Transactions." International Journal of Data Science and Analytics, 9(1), 45-58.
Ometov, A., Bezzateev, S., Mäkitalo, N., Andreev, S., & Koucheryavy, Y. (2018). "Multi-Factor Authentication: A Survey." Cryptography, 2(1), 1-31.
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
Papernot, N., McDaniel, P., Sinha, A., & Wellman, M. (2016). Towards the science of security and privacy in machine learning. arXiv preprint arXiv:1611.03814.
Paxson, V. (2016). Strategies for accelerating AI-powered cybersecurity. ACM Computing Surveys, 50(4), 1-6.
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review, 34(1), 1-14.
Ponemon Institute. (2020). "Cost of a Data Breach Report 2020."
Raff, E., & Nicholas, C. (2020). An introduction to anomaly detection in machine learning. Journal of Machine Learning Research, 21(1), 1-10.
Ruff, L. (2020). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 108(8), 1-24.
Sahin, Y., Bulut, A., & Duman, E. (2021). Performance evaluation of machine learning models for eCommerce fraud detection. Expert Systems with Applications, 182, 115319.
Sahu, D. P., Rajesh, R., & Sreekumar, P. (2021). "Phishing Detection Using NLP and Machine Learning." Journal of Information Security Research, 12(3), 90-103.
Sarkar, S., Das, A., & Roy, D. (2020). "AI-Driven Security Automation with SOAR." Computer Science Review, 28, 100313.
Sarker, I. H. (2021). "Cybersecurity Data Science: An Overview from Machine Learning Perspective." Journal of Big Data, 8(1), 205-232.
Sen, R., Heim, G., & Zhu, Q. (2022). Artificial intelligence and machine learning in cybersecurity: Applications, challenges, and opportunities for mis academics. Communications of the Association for Information Systems, 51(1), 28.
Sengupta, S., Kambhampati, S., & Chaudhuri, B. (2020). "Cyberattack Mitigation with AI-Based Response Automation." Journal of Network and Computer Applications, 164, 102706.
Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security (pp. 1310-1321).
Siddiqui, M. A., Alam, M., & Raza, M. (2019). "Detecting Phishing Emails Using AI and NLP Techniques." Cybersecurity and AI, 6(4), 297-309.
Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305-316).
Sommers, J., & Barford, P. (2012). Analyzing network traffic anomalies. Communications of the ACM, 55(9), 57-64.
Srinivas, M., Reddy, G. R., & Govardhan, A. (2019). "A Review on Security Threats and Vulnerabilities in Cloud Computing." Journal of Cyber Security and Mobility, 8(3), 345-367.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2014). Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199.
Tian, X., Liu, Q., & Li, F. (2021). "Autonomous Detection and Mitigation of DDoS Attacks with AI." Future Generation Computer Systems, 119, 118-129.
Verizon. (2021). "Data Breach Investigations Report."
Vincent, J., Pridgen, A., & Schultz, E. (2018). Mitigating DDoS attacks using AI-based traffic management. IEEE Security & Privacy, 16(5), 34-42.
Wang, Q., Guo, W., Liu, Q., & Yang, Y. (2021). Distributed reinforcement learning for security enhancement in Internet of Things. IEEE Internet of Things Journal, 8(4), 2347-2357.
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3(1), 1-40.
Xie, S., Zhu, F., Wang, W., & Wang, Y. (2020). Anomaly detection for security events using machine learning techniques. Journal of Physics: Conference Series, 1518(1), 012032.
Xu, H., Liu, Y., & Carin, L. (2018). Unsupervised anomaly detection using autoencoders. Journal of Machine Learning Research, 19(1), 1-19.
Yu, F. R., Liu, J., & Wang, Y. (2021). Machine learning for wireless communications in the internet of things. IEEE Wireless Communications, 26(1), 99-105.
Zhang, Q., Yang, L., & Han, J. (2020). "Exploring Explainable AI in Cybersecurity: Challenges and Opportunities." IEEE Transactions on Artificial Intelligence, 1(2), 178-192.
Zhang, Z., Ding, M., Wang, X., & Guo, W. (2021). Adversarial attacks and defenses in deep learning for cybersecurity: A comprehensive review. Computers & Security, 102, 102132.
Zheng, Z., Zhao, W., & Yang, B. (2020). Application of machine learning techniques in eCommerce fraud detection. Journal of Artificial Intelligence Research, 69, 23-39.
Ohm Patel, "Building Data Replication System Replication System IPFS Nodes Cluster", International Journal of Science and Research (IJSR), Volume 8 Issue 12, December 2019, pp. 2057-2069, https://www.ijsr.net/getabstract.php?paperid=SR24708023552
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