E-MAIL SPAM DETECTION

Emails have become a ubiquitous means of personal and professional communication, often containing sensitive and confidential information. However, they are also prime targets for cybercriminals who employ techniques such as phishing to obtain private data. This paper proposes an intelligent and efficient email spam detection system that leverages data mining algorithms for classification and association. By extracting multiple features from email content, we improve classification accuracy and efficiency. The system integrates various machine learning algorithms and achieves a 30% reduction in the error rate compared to existing methods. Our approach enhances email spam detection by combining support vector machines with multi-feature extraction and classification.