MRI: Acquisition of Adaptive Cluster for Performance and Forensics Analysis of Distributed Machine Learning

该项目获得了一个计算集群，旨在为机器学习、算法开发和多种环境下的信息保护提供研究机会。在数据挖掘、机器学习计算中评估和分析性能和剩余数据生成的能力，应该可以更好地控制和降低网络安全漏洞的风险。这些增强功能的可用性还将允许使用这些系统进行应用，跨学科研究，使用大规模数据和预测建模的相互关联分析。调查人员系统地测量性能，以支持数据残留的法医分析，以便发现使用此类平台时可能存在的安全风险。该仪器的采购大大扩展了数据挖掘、安全性和取证研究。网络安全、数字取证和数据挖掘研究的核心研究重点使工作计划能够基于分布式计算环境中与性能、算法和数据安全相关的定义问题。获得的仪器和专业知识为该机构提供了支持国家级客户的能力，如美国陆军航空和导弹研究、开发和工程中心、国家卫生研究所（NIH）和其他依赖于有效和安全的分布式学习来分析和处理敏感数据的政府机构。解决取证挑战和性能分析的一个关键问题是，调查人员可以使用一种工具——调优、调整和重新部署环境，让节点离线进行取证检查，确保存在一致的基线，与其他环境进行比较。在他们的学科内运行有意义的实验，同时能够收集有价值的性能和取证数据（允许非数据挖掘和利用数字取证）。提出的自适应集群工具实现了这四个目标。更广泛的影响：计算研究能力为本科生和研究生的研究以及涉及K-12的培训和曝光活动提供了必要的资源。学生将有机会直接利用集群进行研究和课堂活动。获得的技能应直接导致博士生在六个学院和/或研究所实习和长期就业的机会。这所大学为很大比例的农村和经济萧条地区提供服务；非白人学生占34%，女性学生占60%。该仪器为服务项目的队列活动提供了接触机会。

This project, acquiring a computational cluster, aims to provide opportunities for research in machine-learning, algorithm development, and protection of information in multiple environments. The capacity to evaluate and analyze performance and residue data generation in data mining, machine learning computations, should allow better control and less risk of breaches in cybersecurity. The availability of these enhancements would also permit use of these systems for applied, interdisciplinary research using large-scale data and cross-correlation analyses for predictive modeling. The investigators measure performance systematically to support forensic analysis of data residues, in order to detect possible security risks in the use of such platforms. The procurement of the instrumentation yields a significant expansion in data mining, security, and forensics research. Core research foci in cyber security, digital forensics, and data mining research enables a work plan based on defined problems in distributed computing environments related to performance, algorithms, and data security. The gained instrument and expertise provide the institution with the ability to support national level customers such as U.S. Army Aviation & Missile Research, Development and Engineering Center, National Institute of Health (NIH), and other government agencies that depend on effective and secure distributed learning to analyze and process sensitive data. A key issue with solving both the forensics challenges and the performance analysis is having access to an instrument such that investigators can- Tune, adjust, and redeploy environments, - Take nodes offline to be examined forensically, - Ensure a consistent baseline exists against which other environments are compared, and - Run meaningful experiments within their discipline while enabling the collection of valuable performance and forensics data (permitting non-data mining and utilizing digital forensics).The proposed adaptive cluster instrument enables these four goals.Broader Impacts:The computational research capabilities provide essential resources for undergraduate and graduate students' research as well as for training and exposure activities involving K-12. Students will be afforded hands-on opportunities in research and classroom activities directly utilizing the cluster. The skills gained should lead directly to internships and permanent employment opportunities for doctoral students in the six colleges and/or institutes. The university services a high percentage of rural and financially depressed areas; has a 34% enrollment of non-white students, with 60% female enrollment. The instrumentation offers exposure opportunities for cohort activities to Scholarships for Service Program.