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  1. null (Ed.)
    Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain’s security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform. 
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  2. null (Ed.)
    Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain’s security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform. 
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  3. null (Ed.)
    The COVID-19 viral disease surfaced at the end of 2019 and quickly spread across the globe. To rapidly respond to this pandemic and offer data support for various communities (e.g., decision-makers in health departments and governments, researchers in academia, public citizens), the National Science Foundation (NSF) spatiotemporal innovation center constructed a spatiotemporal platform with various task forces including international researchers and implementation strategies. Compared to similar platforms that only offer viral and health data, this platform views virus-related environmental data collection (EDC) an important component for the geospatial analysis of the pandemic. The EDC contains environmental factors either proven or with potential to influence the spread of COVID-19 and virulence or influence the impact of the pandemic on human health (e.g., temperature, humidity, precipitation, air quality index and pollutants, nighttime light (NTL)). In this platform/framework, environmental data are processed and organized across multiple spatiotemporal scales for a variety of applications (e.g., global mapping of daily temperature, humidity, precipitation, correlation of the pandemic to the mean values of climate and weather factors by city). This paper introduces the raw input data, construction and metadata of reprocessed data, and data storage, as well as the sharing and quality control methodologies of the COVID-19 related environmental data collection. 
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  4. With the recent emergence of highly transmissible variants of the novel coronavirus SARS-CoV-2, the demand for N95 respirators is expected to remain high. The extensive use of N95 respirators by the public is susceptible to demand‐supply gaps and raises concern about their disposal, threatening the environment with a new kind of plastic pollution. Herein, we investigated the filtration performance of the N95 respirator by specifically analyzing the structure in the key filtration layers of meltblown nonwoven after decontamination with one and five cycles of liquid hydrogen peroxide, ultraviolet radiation, moist heat, and aqueous soap solution treatments. With the aid of X-ray microcomputed tomography (microCT) analysis, the local structural heterogeneity of the meltblown nonwoven has been unfolded and subsequently correlated with their filtration performance at a face velocity that matched with speaking conditions (∼3.89 m/s). The filtration efficiency results of the N95 respirator remain unaltered after performing one cycle of treatment modalities (except autoclave).

     
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