Advanced Email Security with NLP and the Isolation Forest Algorithm

Email security is crucial in today's digital age, as it serves as the primary means of communication for individuals and businesses alike. Therefore, it is vulnerable to various cyber threats like phishing, malware, and spam. These threats can lead to data breaches, financial losses, and the theft of private information. This study introduces a comprehensive approach to detect unusual patterns in emails using artificial intelligence. It specifically emphasizes the integration of Natural Language Processing (NLP) and the Isolation Forest algorithm. Key contributions encompass techniques for gathering and organizing email datasets, the utilization of TF-IDF vectorization to transform email text into numerical characteristics, the integration of the Isolation Forest algorithm to detect anomalies in email behavior, and the assessment of model effectiveness through metrics like Precision, Recall, F1-Score, and ROC-AUC. In addition, the system demonstrates excellent scalability, flexibility, and accuracy when it comes to real-world email security scenarios. This demonstrates the effectiveness of combining natural language processing (NLP) with machine learning (ML) to enhance email security and offers a robust solution for detecting and mitigating cyber-attacks.