AI-Based Phishing Attack Detection And Prevention Using Natural Language Processing (NLP)

Abstract

Phishing attacks remain one of the most prevalent and damaging cybersecurity threats, targeting users across various communication channels such as email, social media, and SMS. Traditional phishing detection systems are often limited to email and rely on static rule-based filtering or keyword matching, making them ineffective against evolving phishing tactics. This project proposes an innovative solution that utilizes Artificial Intelligence (AI) and Natural Language Processing (NLP) to create a real-time phishing attack detection and prevention system. By analyzing the contextual language of messages across multiple platforms, the system can detect and block phishing attempts with high accuracy. The system extracts important linguistic features such as urgency, emotional tone, and anomalous patterns within text, and applies machine learning algorithms—such as Random Forest, Support Vector Machines (SVM), and deep learning models like Long Short-Term Memory Networks (LSTM)—for classification. Additionally, a feedback loop is integrated to allow the system to adapt and improve over time through active learning, ensuring the detection system evolves alongside emerging phishing techniques. This AI-based solution extends beyond traditional email phishing detection by incorporating multiple channels, including SMS and social media platforms, making it a versatile tool for individuals and businesses. The system offers automated prevention actions, such as flagging suspicious messages and alerting users, thus providing a robust defense against phishing attacks in real-time. The project's implementation aims to fill the market gap in comprehensive, multi-channel phishing detection and contribute to the growing demand for intelligent and adaptive cybersecurity solutions.