No abstract

The growth in scale and capacity of networks in recent years leads to challenges of positioning and scalability of Intrusion Detection Systems (IDS). With the flexibility afforded by programmable dataplanes, it is now possible to perform a new level of intrusion detection in switches themselves. We present P4ID, combining a rule parser, stateless and stateful packet processing using P4, and evaluate it using publicly available datasets. We show that using this technique, we can achieve a significant reduction in traffic being processed by an IDS.

Abstract-WorldMap is a web-based, map centric data exploration system built on open source geospatial technology at Harvard University. It is designed to serve collaborative research and teaching, but is also accessible to the general public. This paper explains WorldMap's basic functions through several historical research projects, demonstrating its flexible scale (from neighbourhood to continent) and diverse research themes (social, political, economical, cultural, infrastructural, etc.). Also shared are our experiences in handling technical and institutional challenges during system development, such as synchronization of software components being developed by multiple organizations; juggling competing priorities for serving individual requests and developing a system that will enable users to support themselves; balancing promotion of the system usage with constraints on infrastructure investment; harnessing volunteered geographic information while managing data quality; as well as protecting copyrights, preserving permanent links and citations, and providing long term archiving.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.