DESIGN OF AN INTELLIGENT DEEP LEARNING FRAMEWORK FOR PHISHING URL DETECTION IN CYBERSECURITY APPLICATIONS

Phishing is the ubiquitous cyber threat from social engineering and web-deception to compromise user security, which may lead to credential theft, financial loss and data breaches. Originally blacklist-based detection methods cannot be used since the nature of phishing attacks varies and has changed frequently. We present our novel deep learning methodology for phishing URL detection based upon the publicly available PhiUSIIL dataset of large-scale lexical, host-based, content-based URL features in this paper. The new model employs sophisticated feature engineering, correlation analysis, and deep learning techniques to estimate discriminative patterns between benign and malicious URLs. This tool was implemented by a multi-layered deep neural network optimized with Adam optimizer and binary cross-entropy loss with dropout regularization to avoid overfitting. There was an extensive study of the model on the other model classesifiers of machine learning called Logistic Regression, Random Forest and Support Vector Machines. The results show that the deep learning model stands apart from the base models as well with 99.4% accuracy, 98.1% precision, 97.9% recall, 98.0% F1-score, and a strong generalization in absence of test samples. This confirms that the intelligent deep learning model proposed provides ascalable, adaptive, and robust method for phishing URL detection. This framework can be applied to any security applications in real-world such as browser extensions, email filters, and intrusion detection systems to actively defend against phishing threats. For future extensions, hybrid deep learning models will be studied, explained AI processes, and deployment in real-time detection.