Machine Learning Algorithms for E-Commerce Security

Abstract

In the current era, several machine learning algorithms play a major role in supporting e-commerce security by offering a practical approach to detecting and mitigating threats. These techniques and methods provide a clear analysis of vast amounts of transactional data to identify suspicious activities and patterns indicative of fraud. Techniques such as anomaly detection, clustering, and classification help in real-time threat identification and response. Over the years we have seen a boom in e-commerce where people are moving to online shopping for their regular stuff. However, it also means the drastic rise of e-commerce fraud and hacking. The main purpose of selecting machine learning algorithms is that it can learn from data and use this learned knowledge for predicting new examples or making decisions. This chapter gives clear ideas and best practices approaches on machine learning algorithms for e-commerce on security aspects. These algorithms used in machine learning models help identify and detect fraud access systems, personalized security measures, and automated threat response mechanisms.