Intelligent Phishing Detection Using Deep Neural Networks

User account fraud persists as one of the most prevalent cybercrimes worldwide, primarily due to inadequate protection mechanisms for personally identifiable information The evolution of phishing attack methodologies has been further enabled by diminishing user vigilance, a consequence of information overload in digital environments. Contemporary malware employs sophisticated social engineering strategies to establish credibility with targets while simultaneously integrating anti-detection measures that bypass traditional signature and heuristic-based defenses.In this paper, we propose a novel framework for phishing website identification and forensic analysis. Our approach extracts discriminative features from both URL structures and webpage content to classify malicious sites with high accuracy. Furthermore, we investigate the application of deep convolutional neural networks for phishing detection, demonstrating their superior performance compared to conventional machine learning models. Experimental results validate that our optimized architecture achieves a detection rate of 98.7%, significantly outperforming existing solutions referenced in prior literature