In the past few years there has been a growing desire to provide more built in functionality to protect user communications from eavesdropping. An example of this is DNS over HTTPS (DoH) which can be used to protect user privacy, confidentiality and against spoofing attacks. Since its first popularity in 2018 as used in browsers, there is much further study to test the effectiveness of DoH in protection schemes and whether it is possible to detect the protocol over the web. Detecting DoH traffic among normal web traffic is also a major challenge for network admins to allow filtering of malicious traffic flows. In this paper, we investigate machine learning classification to study the detection of DoH traffic and further analyze the key feature characteristics in the protocol behavior to help researchers build credibility in the DoH protocol detection. Our study reveals key features and statistical relationships among DoH test runs on the Alexa-recommended 100 most-used websites using three different DoH servers, showing up to 98% test accuracy in our built classifier.

This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-At-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.