Biblio

Applying machine learning techniques to detect malicious encrypted network traffic has become a challenging research topic. Traditional approaches based on studying network patterns fail to operate on encrypted data, especially without compromising the integrity of encryption. In addition, the requirement of rendering network-wide intelligent protection in a timely manner further exacerbates the problem. In this paper, we propose to leverage ×86 multicore platforms provisioned at enterprises' network edge with the software accelerators to design an encrypted traffic analytics (ETA) system with accelerated speed. Specifically, we explore a suite of data features and machine learning models with an open dataset. Then we show that by using Intel DAAL and OpenVINO libraries in model training and inference, we are able to reduce the training and inference time by a maximum order of 31× and 46× respectively while retaining the model accuracy.

Network flow classification is fundamental to network management and network security. However, it is challenging to classify network flows at very high line rates while simultaneously preserving user privacy. Machine learning based classification techniques utilize only meta-information of a flow and have been shown to be effective in identifying network flows. We analyze a group of widely used machine learning classifiers, and observe that the effectiveness of different classification models depends highly upon the protocol types as well as the flow features collected from network data.We propose vTC, a design of virtual network functions to flexibly select and apply the best suitable machine learning classifiers at run time. The experimental results show that the proposed NFV for flow classification can improve the accuracy of classification by up to 13%.