Jacobus Van der Merwe

Anirudh Kamath, Dustin Maas, Jacobus Van der Merwe et al.

Data wrangling continues to be the most time-consuming task in the data science pipeline and wireless network data is no exception. Prior approaches for automatic or assisted data-wrangling primarily target unordered, single-table data. However, unlike traditional datasets where rows in a table are unordered and assumed to be independent of each other, wireless network datasets are often collected across multiple measurement devices, producing multiple, temporally ordered tables that must be integrated for obtaining the complete dataset. For instance, to create a dataset of the signal quality of 5G cell towers within a geographic region, GPS data collected by cellphones must be joined with radio frequency measurements of the corresponding cell towers. However, the join key timestamp typically exhibits mismatched sampling periods, causing a misalignment. Data wrangling techniques for generic time-series datasets also fail here, since they lack knowledge of domain-specific data semantics, which are often defined by network protocols and system configurations. To aid in wrangling wireless network datasets, we demonstrate WN-Wrangle, an interactive wrangling assistant, tailored to the wireless network domain that suggests the top-k next-best wrangling operations, along with rich, domain-specific explanations. Under the hood, WN-Wrangle enforces temporal constraints- and a wireless network semantics-aware mechanism to score and rank an extended set of wrangling operators to improve the data quality. We demonstrate how WN-Wrangle identifies elusive data-quality issues specific to the wireless network domain and suggests accurate wrangling steps over datasets obtained from the widely used POWDER city-scale wireless testbed.

Jiahui Chen, Joe Breen, Jeff M. Phillips et al.

Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable classification framework is ideal, as it can be customized for use in a wide variety of networks. In this paper, we propose a highly configurable and flexible machine learning traffic classification method that relies only on statistics of sequences of packets to distinguish known, or approved, traffic from unknown traffic. Our method is based on likelihood estimation, provides a measure of certainty for classification decisions, and can classify traffic at adjustable certainty levels. Our classification method can also be applied in different classification scenarios, each prioritizing a different classification goal. We demonstrate how our classification scheme and all its configurations perform well on real-world traffic from a high performance computing network environment.