AI-Driven Phishing Detection System Using Multi-Modal Inputs

Phishing attacks remain a serious threat to cybersecurity. They take advantage of both human and system weaknesses to steal sensitive information. Traditional detection methods, which usually focus on single types of features like URLs or email content, have a hard time keeping up with more complex phishing tactics. This research offers an AI-driven phishing detection system that uses multiple types of inputs. It combines text, visuals, and behavioral data to improve detection accuracy.The system uses natural language processing (NLP) to analyze the content of emails and URLs. It employs convolutional neural networks (CNNs) to evaluate website screenshots and uses behavioral analytics to track user interaction patterns. By merging these different methods, the framework can spot subtle phishing signs that single-modal systems might miss. Experimental results on benchmark datasets show that this system outperforms traditional methods in terms of precision, recall, and F1-score. It also keeps low latency, making it suitable for real-time use. The proposed system offers a scalable, smart, and strong solution for detecting phishing, enhancing cybersecurity against changing threats.