Combatting Phishing Threats: An NLP-Based Programming Approach for Detection of Malicious Emails and Texts

Attackers are employing more advanced strategies to trick people into divulging private information or carrying out harmful deeds, and phishing is still a serious cybersecurity risk. We provide a new method in this study that combines algorithms based on AI-based expert systems and deep learning (ML) with the use of NLP-based programming (NLP) approaches to identify fraudulent emails and messages. Using a variety of datasets that include samples of both authentic and phishing messages, our approach preprocesses textual data, extracts relevant characteristics, and trains AI-based expert systems and deep learning models. Metrics including accuracy, precision, recall, and F1-score are used to assess the effectiveness of different AI-based expert systems and deep learning methods, such as logistic regression, random forests, decision trees, and neural networks, among others. To collect semantic information and increase detection accuracy, we also investigate the integration of sophisticated NLP-based techniques, such as word embeddings. The efficacy of our suggested strategy in reducing this common cybersecurity issue is highlighted by our results, which show promising performance in correctly recognizing phishing attempts.