Paper Title
A Machine Learning-Based Phishing Website Detection System

Phishing attacks are prevalent and pose a significant threat to cybersecurity [11]. This paper introduces a novel machine learningbased system for the detection of phishing websites. The proposed system leverages Natural Language Processing (NLP) techniques and Word Vectors for enhanced detection accuracy[5]. We rigorously evaluate various machine learning algorithms, including Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM), employing different feature sets generated from NLP and Word Vectors. Our results indicate that the Random Forest algorithm, when combined with NLP-based features, achieves the highest accuracy of 97.99%[7]. This research contributes to more effective real-time phishing website detection systems, thereby enhancing online security. Keywords - Phishing, Cybersecurity, Deception, Digital Threats, Malicious Actors, Cyber Attacks, Online Vulnerabilities, Trust Exploitation, Credential Theft, Malware Delivery, Machine Learning, Natural Language Processing, Detection Systems.