The exponential growth of email communication has led to an increase in unsolicited and potentially harmful spam messages, posing significant challenges to both users and organizations. Traditional spam detection techniques primarily focus on classification accuracy while often neglecting data security and privacy concerns. This paper presents a secure and efficient email spam detection system that integrates machine learning with cryptographic techniques. The proposed approach utilizes Support Vector Machine (SVM) for effective classification of emails based on textual features. To ensure data confidentiality, Advanced Encryption Standard (AES) is employed for encrypting email content, while Elliptic Curve Cryptography (ECC) is used for secure key exchange. The integration of classification and encryption mechanisms enables the system to provide reliable spam detection while preserving sensitive information. The proposed framework is suitable for real-world applications where both accuracy and data privacy are essential.
Priyadarshini et al. (Thu,) studied this question.