Integrating Natural Language Processing and AI for Phishing Website Detection

Phishing websites are a significant cybersecurity threat that aim to deceive users into divulging sensitive information, such as login credentials or financial data. Recent advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) have made it possible to identify these malicious sites with greater accuracy. This paper explores the integration of NLP and AI for phishing website detection, examining current methodologies, algorithms, and tools. The paper also discusses challenges, datasets, and future directions in this domain. The proliferation of phishing websites constitutes a critical threat to digital security, enabling fraud, compromising sensitive data, and infringing on individual privacy. This research addresses the challenge by developing and evaluating a machine learning-based detection system. We implement a comparative analysis of multiple algorithmic approaches to identify the most effective strategy for distinguishing malicious sites from legitimate ones. Our methodology begins with a survey of current techniques in the domain to inform model selection and feature engineering. We then train and test a diverse array of seven core machine learning classifiers: Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), Random Forest (RF), Support Vector Classifier (SVC) with radial basis function kernel, Linear Support Vector Classifier (Linear SVC), and Naïve Bayes (NB) The study’s core contribution lies in a detailed evaluation of each model's performance metrics, computational efficiency, and suitability for the task. We specifically examine trade-offs such as accuracy versus interpretability, and training time versus predictive precision to provide clear guidelines on selecting an algorithm based on operational constraints, whether for high-throughput screening or for forensic analysis requiring model transparency.