A Hybrid Deep Learning and Anomaly Detection Framework for Real-Time Malicious URL Classification

Malicious URLs remain a primary vector for phishing, malware, and cyberthreats. This study proposes a hybrid deep learning framework combining textttHashingVectorizer n-gram analysis, SMOTE balancing, Isolation Forest anomaly filtering, and a lightweight neural network classifier for real-time URL classification. The multi-stage pipeline processes URLs from open-source repositories with statistical features (length, dot count, entropy), achieving O(NL + EBdh) training complexity and a 20,ms prediction latency. Empirical evaluation yields 96.4% accuracy, 95.4% F1-score, and 97.3% ROC-AUC, outperforming CNN (94.8%) and SVM baselines with a 50!×--100!× speedup (Table~reftab:comp-complexity). A multilingual Tkinter GUI (Arabic/English/French) enables real-time threat assessment with clipboard integration. The framework demonstrates superior scalability and resilience against obfuscated URL patterns.