Building scalable and real-time deep learning classification of encrypted network traffic

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With increasing use of cybersecurity-based solutions, most data transmitted over networks is secured either by encryption or similar techniques. While this is essential for security and the protection of data, network traffic classification remains challenging. This is further convoluted when large-scale data is generated at high speed. This requires classification of encrypted network traffic data that is capable of (1) obviating deep packet or content inspection, and (2) maintaining high accuracy on big data produced at high speed, both of which will be addressed by the proposed project. Deep learning is part of machine learning that focuses on learning data representations and is inspired by the structures and functions of the human brain known as artificial neural networks. We will lay the foundation of a framework that will be based on machine learning and deep learning to perform efficient classification of encrypted network traffic data. The outcomes from this research will be used by the industry partner as potential enhancements and extensions to their current products and for developing new products.