Design and Implementation of an AI-Based Email Spam and Phishing Detection System
DOI:
https://doi.org/10.15662/IJEETR.2026.0802249Keywords:
Email Spam Detection, Phishing Detection, Artificial Intelligence, Machine Learning, Natural Language Processing, Layered Firewall, IP Obfuscation, Deep Learning, Cybersecurity, Email Classification, TF-IDF, Random ForestAbstract
The rapid growth of email communication has significantly increased the risk of spam and phishing attacks, posing serious threats to individuals and organizations worldwide. Traditional email filters often struggle to provide fast detection, adaptive security, and protection against sophisticated attackers who frequently change identities and network parameters. To overcome these limitations, this paper proposes an AI-Based Email Spam and Phishing Detection System designed for high-speed detection and enterprise-level email traffic of up to 232 Mbps. The system employs a layered model to independently analyze email headers, content, URLs, and sender , ensuring robust protection against complex and multi-stage phishing attacks. Advanced AI techniques including machine learning classification and Natural Language Processing (NLP) intelligently classify spam and phishing emails by learning patterns from content, behavior, and traffic characteristics, continuously adapting to emerging threats. The framework further incorporates IP obfuscation and interchanging mechanisms to reduce exposure to reconnaissance and network exploitation attempts. Experimental evaluation demonstrates high detection accuracy, low response times, and enhanced network security, offering a fast, intelligent, and scalable email protection solution against evolving spam and phishing threats.
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