Development of an Anti-Phishing Model for Detecting Cyber Crimes
Phishing attacks remain a pervasive and destructive form of cybercrime, leveraging social engineering to trick users into divulging sensitive information. Despite continuous advancements in security measures, attackers persistently refine their tactics, rendering traditional defenses often inadequate. This study introduces a robust anti-phishing model engineered for effective detection and mitigation of these evolving threats. The model integrates machine learning algorithms, specifically Random Forest (RF), with Natural Language Processing (NLP) techniques to analyze diverse features, including Uniform Resource Locator (URL) structures, email headers, and content patterns. Critical features such as domain age, Secure Hyper Text Markup Language (HTTPS) presence, and keyword patterns were meticulously selected through correlation analysis and recursive feature elimination (RFE). The model demonstrates high accuracy value of 0.965 representing 96.5%, precision value of 0.952 representing 95.2%, and recall value of 0.978 representing 97.8%, affirming its efficacy in identifying malicious activities. Furthermore, adaptive algorithms, dynamic learning mechanisms, and real-time threat intelligence integration are employed to ensure resilience against contemporary and future phishing tactics. This research underscores the critical importance of synergizing automated systems with robust user-awareness strategies to fortify defenses against phishing attacks. The findings contribute significantly to the development of scalable, adaptive cybersecurity solutions essential for safeguarding sensitive information across diverse online environments.
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