Automated Spam Detection Using Sandpiper Optimization Algorithm-Based Feature Selection with the Machine Learning Model

The email has become an online communication tool and an important part of daily life. Spam mails take up a lot of space and bandwidth, and spam filtering algorithms have flaws that cause them to mistake legitimate emails for spam (false positives). These issues are becoming a bigger difficulty for the online world. This work proposes the use of a sandpiper optimization (SPO) algorithm, which is applied for the feature selection process which minimizes the training complexity and maximizes the classification accuracy and Radial Bias Neural Network (RBNN) for classifying emails as genuine email and spam email. The Enron email dataset and Spam Assassin datasets were used. The outcomes show that the rotation forest algorithm after feature selection with SPO accurately classifies the emails as genuine email and spam email with 3.88%, 5.75%, and 6.16% higher accuracy for Genuine Email, 2.31%, 8.47%, and 7.23% higher accuracy for spam email compared with existing Universal Spam Detection using Transfer Learning of the BERT Model (USD-TL-BERT), Hybrid Learning Approach for E-mail Spam Detection and Classification (HLA-ESDC), and the Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method (ESD-HAHD), respectively. This demonstrates that the proposed method significantly outperforms existing methods.