DNNLSTM Algorithms for Comparative Study on Machine Learning Approach for Malicious Study - Nafees, Shabeena; Pandey, Anil Kumar; Verma, Satya Bhushan (Journal of Neonatal Surgery, 2025)
E-commerce systems, utilizing the Internet for various functions such as banking, shopping, and sales, offer numerous advantages to customers. However, security remains a challenge, as the numerous advantages of these systems come with the potential for cyber threats. Therefore, it is crucial to carefully consider the security measures in these systems. Online transactions in the e-commerce system are increasing rapidly, posing a significant risk to consumers' personal information. To ensure the Internet safety, it is crucial to address vulnerabilities in the system's components, which can be accidental or malicious, at entry and exit points. The number of threats in the system was well managed through the usage of machine learning techniques, ensuring efficient detection and prevention. E-commerce systems require robust security measures to ensure their functionality and safety, a task that researchers working in this field are well-equipped to tackle. This paper focuses on analysing security in e-commerce systems using machine learning.