Machine learning is leveraged for many network traffic analysis tasks in security, from application identification to intrusion detection. Yet, the aspects of the machine learning pipeline that ultimately determine the performance of the model---feature selection and representation, model selection, and parameter tuning---remain manual and painstaking. This paper presents a method to automate many aspects of traffic analysis, making it easier to apply machine learning techniques to a wider variety of traffic analysis tasks. We introduce nPrint, a tool that generates a unified packet representation that is amenable for representation learning and model training. We integrate nPrint with automated machine learning (AutoML), resulting in nPrintML, a public system that largely eliminates feature extraction and model tuning for a wide variety of traffic analysis tasks. We have evaluated nPrintML on eight separate traffic analysis tasks and released nPrint, nPrintML and the corresponding datasets from our evaluation to enable future work to extend these methods.

Nearly every service on the Internet relies on the Domain Name System (DNS), which translates a human-readable name to an IP address before two endpoints can communicate. Today, DNS traffic is unencrypted, leaving users vulnerable to eavesdropping and tampering. Past work has demonstrated that DNS queries can reveal a user’s browsing history and even what smart devices they are using at home. In response to these privacy concerns, two new protocols have been proposed: DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT). Instead of sending DNS queries and responses in the clear, DoH and DoT establish encrypted connections between users and resolvers. By doing so, these protocols provide privacy and security guarantees that traditional DNS (Do53) lacks.

In this work, we examine to what extent the Internet's routing infrastructure needlessly exposes network traffic to nations geographically irrelevant to packet transmission. We quantify what countries are geographically logical to see on a network path traveling between two nations through the use of convex hulls circumscribing major population centers, and then compare that to the nation states observed in utilized paths. Our preliminary results show that the majority of paths, 52%, unnecessarily expose traffic to at least one nation. We also explore which nation states are disproportionately allowed to observe and manipulate a larger fraction of Internet traffic than they otherwise should.