Extended application of random-walk shielding-potential viscosity model of metals in wide temperature region

The transport properties of matter have been widely investigated. In particular, shear viscosity over a wide parameter space is crucial for various applications, such as designing inertial confinement fusion (ICF) targets and determining the Rayleigh-Taylor instability. In this work, an extended random-walk shielding-potential viscosity model (RWSP-VM) [Phys. Rev. E 106, 014142] based on the statistics of random-walk ions and the Debye shielding effect is proposed to elevate the temperature limit of RWSP-VM in evaluating the shear viscosity of metals. In the extended model, we reconsider the collision diameter that is introduced by hard-sphere concept, hence, it is applicable in both warm and hot temperature regions (10^1-10^7 eV) rather than the warm temperature region (10^1-10^2 eV) in which RWSP-VM is applicable. The results of Be, Al, Fe, and U show that the extended model provides a systematic way to calculate the shear viscosity of arbitrary metals at the densities from about 0.1 to 10 times the normal density (the density at room temperature and 1 standard atmosphere). This work will help to develop viscosity model in wide region when combined with our previous low temperature viscosity model [AIP Adv. 11, 015043].Comment: 6 pages, 5 figure

Querying Streaming System Monitoring Data for Enterprise System Anomaly Detection

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely abnormal system behavior detection over the stream of monitoring data. However, existing stream-based solutions lack explicit language constructs for expressing anomaly models that capture abnormal system behaviors, thus facing challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale monitoring data. To address these limitations, we build SAQL, a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomaly models. SAQL provides a domain-specific query language, Stream-based Anomaly Query Language (SAQL), that uniquely integrates critical primitives for expressing major types of anomaly models. In the demo, we aim to show the complete usage scenario of SAQL by (1) performing an APT attack in a controlled environment, and (2) using SAQL to detect the abnormal behaviors in real time by querying the collected stream of system monitoring data that contains the attack traces. The audience will have the option to interact with the system and detect the attack footprints in real time via issuing queries and checking the query results through a command-line UI.Comment: Accepted paper at ICDE 2020 demonstrations track. arXiv admin note: text overlap with arXiv:1806.0933