Unleashing the Potential of Machine Learning and NLP Contextual Word Embedding for URL-Based Malicious Traffic Classification

With the increasing prevalence of cyber threats, the demand for efficient and effective malware detection systems has reached unprecedented levels. This research paper presents a novel approach to detecting malware packets based on URL analysis, utilizing natural language processing (NLP) techniques. Traditional malware detection methods rely heavily on statistical approaches and anomaly detection techniques, which have inherent limitations in detecting complex and rapidly evolving malware. In contrast, our proposed approach harnesses the power of NLP to examine the payload of network traffic and identify malicious packets by analyzing specific text patterns found in the URLs in the payload. In this research, we achieved sparsity problems with TF-IDF vectorization and also demonstrated that our proposed approach, deploying the ROBERTa model in a real-world network, achieves exceptional detection rates while maintaining low false-positive rates, i.e., 2%, where as random forest 7.1 % and SVM 13.8%. It surpasses statistical methods and other NLP-based models in terms of malware packet detection. Compared to random forest (90.2% accuracy) and SVM (79.0% accuracy), which are powerful in classification, our ROBERTa-based approach achieves an impressive accuracy of 99.6 %. Moreover, our approach exhibits greater resilience against adversarial attacks as it does not rely on fixed signatures or patterns.