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

Abstract

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.

KEYWORDS:

• Feature selection
• Fire Hawks Optimizer
• Machine learning
• Spam detection

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

T. Amutha

T Amutha is currently working at Care College of Engineering, Trichy, Tamil Nadu as an assistant professor in the Department of Artificial Intelligence and Data Science. She has 22 years of teaching experience.

S. Geetha

S Geetha is currently working as an assistant professor (senior grade), in the Department of Computer Applications, University College of Engineering, Bharathiidasan Institute of Technology Campus, Anna University, Thiruchirappalli, Tamil Nadu, India. Email: [email protected]