PREDICTING PHISHING ATTACKS USING NATURAL LANGUAGE PROCESSING AND USER BEHAVIORAL INDICATORS

Abstract

Phishing attacks continue to be among the most pervasive and damaging threats in the cyber security landscape, leveraging both technical vulnerabilities and human cognitive biases to manipulate users into disclosing sensitive information such as login credentials, financial data, and personal identifiers. Traditional detection mechanisms, including rule-based and signature-based systems, often fail to keep pace with the rapidly evolving linguistic patterns, sophisticated social engineering tactics, and polymorphic strategies employed by attackers in modern phishing campaigns. Addressing this challenge requires an integrated approach that considers both the content of phishing messages and the behavioral characteristics of users interacting with them. This research proposes a comprehensive framework for predicting phishing attacks by combining advanced Natural Language Processing (NLP) techniques with user behavioral indicators. The study examines semantic, syntactic, and pragmatic features of emails and messaging content to capture subtle cues of phishing attempts. In parallel, it analyzes user interaction patterns, including click behaviors, response times, and historical susceptibility to phishing, thereby recognizing the socio-technical dimensions of cyber security threats. A supervised machine learning methodology is employed, utilizing benchmark phishing datasets augmented with simulated behavioral data to enhance model generalizability. The findings indicate that hybrid models integrating content analysis with behavioral features significantly outperform content-only approaches. These models achieve higher precision, recall, and overall robustness, demonstrating the ability to detect previously unseen phishing strategies with greater reliability. The study underscores the importance of accounting for human factors alongside technical indicators in cybersecurity research, highlighting the need for proactive, human-centered defense mechanisms. By emphasizing the interplay between linguistic cues and user behavior, this research contributes to the development of predictive and adaptive phishing detection systems. The results offer practical implications for organizations, suggesting strategies for implementing training programs, awareness campaigns, and AI-driven monitoring tools that target high-risk behaviors while enhancing overall email security. This integrated approach provides a pathway toward more resilient cyber security infrastructures capable of responding effectively to increasingly sophisticated phishing attacks.