Machine Learning and Big Data Approaches to Enhancing E-commerce Anomaly Detection and Proactive Defense Strategies in Cybersecurity

Abstract

The rapid proliferation of e-commerce platforms has made them prime targets for cybercriminals employing increasingly sophisticated tactics. To safeguard sensitive data, ensure transactional integrity, and preserve consumer trust, e-commerce platforms must adopt advanced technologies for anomaly detection and proactive cybersecurity defense. Machine learning (ML) and big data analytics present transformative opportunities in this domain by enabling real-time monitoring, anomaly detection, and predictive insights. This paper explores the synergistic application of machine learning and big data to enhance e-commerce cybersecurity. It delves into machine learning algorithms, such as supervised, unsupervised, and reinforcement learning, and highlights their role in identifying fraudulent patterns, predicting potential breaches, and automating defensive responses. Concurrently, the paper examines the integration of big data techniques, emphasizing their capacity to handle vast datasets generated by e-commerce platforms and their importance in generating actionable insights. The main sections discuss state-of-the-art methods, challenges in implementation, and future trends. By combining big data analytics with ML-driven models, e-commerce platforms can achieve anomaly detection systems that are not only accurate but also adaptive to evolving cyber threats. Furthermore, the proactive nature of these technologies empowers businesses to mitigate risks preemptively rather than reactively. This paper concludes with a discussion of the practical implications of these approaches and recommendations for further research.