AI-Driven Deep Learning Framework for Identifying Malicious Activities in Encrypted Network Traffic

The increasing adoption of encrypted network communications has improved data privacy but also introduced new challenges in detecting malicious cyber activities hidden within encrypted traffic. Traditional signature-based and rule-driven detection systems are ineffective in analyzing encrypted data streams without decryption, leading to significant blind spots in network defense. This study proposes a deep learning-based approach that leverages flow metadata, behavioral patterns, and statistical features to identify malicious activities in encrypted traffic without compromising data confidentiality. Various deep learning architectures, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer models, were evaluated for anomaly detection, behavioral profiling, and encrypted traffic classification. Experimental results demonstrate that deep learning models can effectively detect malicious behavior in encrypted data streams with high accuracy, reduced false positives, and improved generalization across diverse threat scenarios. The findings highlight the potential of privacy-preserving threat detection techniques to enhance network security without decrypting sensitive data. This research also underscores the importance of adaptive learning mechanisms, model explainability, and real-time deployment strategies for practical implementation in modern cybersecurity environments.