An Innovative ML Strategy for Identifying Email Phishing Threats in Financial Cloud Infrastructure

Phishing emails are a serious threat in financial cloud systems because they utilize deceptive information to trick users into opening unsanctioned accounts or stealing private data. To protect financial data and maintain the integrity of cloud-based financial systems. In this research, we aim to develop an innovative machine learning (ML) based strategy for identifying email phishing threats in financial cloud infrastructure. In a phished email, the sender can deceive the user into providing confidential data. Phishing emails is the primary concern while sending and receiving emails. For this, we proposed a novel Bald Eagle tuned Versatile Random Forest (BE-VRF) algorithm for identifying email phishing in a financial cloud environment. Initially, This study gathered the phish tank dataset for this study. After gathering the data we employed a stop word removal for the preprocessing method. We employed Independent Component Analysis (ICA) to extract the crucial features from the processed data. The suggested BE-VRF algorithm classifies the email texts and it can identify the abnormal mails. We implemented our suggested model in Python software. The performance evaluation phase employs various metrics such as f1 score, recall, accuracy, MAPE, and precision to examine the efficacy of suggested model. The evaluation study is done with different existing methods and the result express that the recommended model obtained improved outcome than other predictable methods, for identifying email phishing threats in financial cloud infrastructure.