Safeguarding E Commerce and Preventing Financial Fraud with AdaBoost CNN Cybersecurity Approach

Examining the cyber security risks faced by companies and e-commerce platforms is the focus of this research. People in the corporate world and those in academia are curious about the technology applications of e-commerce. There are now possibilities that were before unimaginable for both businesses and consumers. On the other hand, there were certain issues with its arrival, chief among them being cyber security. Among the many forms of cybercrime, this study focuses on social engineering, denial-of-service attacks, malware, and data breaches. This proposed approach consists of three phases, which are feature extraction, model training, and data preprocessing. Addressing missing values, normalizing features, and eliminating noise are all part of preprocessing, which aims to clean and organize raw data. When dealing with multivariate data, principal component analysis (PCA) is a common tool for feature extraction. Throughout the training process, we employed an AdaBoost-CNN. With an average accuracy of 90.65%, this innovative technique surpasses CNN and AdaBoost