Encrypted Traffic Analysis for Malware Detection Using Deep Learning

In an era where cyber threats continually evolve, the detection of malware within encrypted network traffic remains a formidable challenge. This study presents a pioneering approach that harnesses the power of deep learning to address this challenge effectively. The proposed methodology for “Encrypted Traffic Analysis for Malware Detection using Deep Learning” leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models to extract meaningful features from encrypted data. Proposed research not only significantly enhances malware detection accuracy but also contributes to robustness against adversarial attacks. Additionally, this work emphasizes the importance of real-time analysis, scalability, and privacy preservation in the proposed approach. Cross-dataset evaluation further demonstrates the generalizability of our models, and continuous monitoring ensures adaptability to evolving malware threats. With these contributions, our methodology marks a significant advancement in the realm of network security, safeguarding systems against hidden malware threats lurking within encrypted traffic.