| Title | ACETA: Accelerating Encrypted Traffic Analytics on Network Edge |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Manning, Derek, Li, Peilong, Wu, Xiaoban, Luo, Yan, Zhang, Tong, Li, Weigang |
| Conference Name | ICC 2020 - 2020 IEEE International Conference on Communications (ICC) |
| Date Published | jun |
| Keywords | Acceleration, cryptography, DAAL, Data models, edge computing, Encrypted Traffic Analysis, feature extraction, Libraries, machine learning, Metrics, multicore computing security, OpenVINO, pubcrawl, resilience, Resiliency, Scalability, Training, uCPE |
| Abstract | 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 x86 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 31x and 46x respectively while retaining the model accuracy. |
| DOI | 10.1109/ICC40277.2020.9148798 |
| Citation Key | manning_aceta_2020 |
ACETA: Accelerating Encrypted Traffic Analytics on Network Edge