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

MRI：获取自适应集群以实现分布式机器学习的性能和取证分析

• 批准号: 1726069
• 负责人: Ryan Benton
• 金额: $ 11.51万
• 依托单位: University of South Alabama
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1726069&HistoricalAwards=false
• 关键词: MRI; Acquisition; Adaptive; Cluster; Performance

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.

这个项目，获取一个计算集群，旨在为在多种环境中研究机器学习、算法开发和信息保护提供机会。评估和分析数据挖掘和机器学习计算中的性能和剩余数据生成的能力，应该能够更好地控制和降低网络安全中的漏洞风险。这些改进的提供还将允许使用这些系统进行应用的跨学科研究，使用大规模数据和相互关联分析进行预测建模。调查人员系统地衡量业绩，以支持对数据残留物进行法医分析，以检测在使用这类平台时可能存在的安全风险。仪器的采购在数据挖掘、安全和取证研究方面产生了显著的扩展。专注于网络安全、数字取证和数据挖掘研究的核心研究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已定义问题。获得的仪器和专业知识使该机构有能力支持国家级客户，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能够访问工具，以便调查人员可以调整、调整和重新部署环境，-使节点离线进行取证检查，-确保存在与其他环境进行比较的一致基线，以及-在其学科内运行有意义的实验，同时支持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)。建议的自适应集群工具实现了这四个目标。广泛的影响：计算研究能力为本科生和研究生的研究以及与K-12有关的培训和曝光活动提供了必要的资源。学生将获得直接利用集群进行研究和课堂活动的实践机会。所获得的技能应直接为博士生在六个学院和/或研究所提供实习机会和永久就业机会。这所大学为农村和经济困难地区提供服务的比例很高；非白人学生的入学率为34%，女性入学率为60%。该仪器为队列活动提供了接触服务奖学金计划的机会。

项目成果

Big Data Forensics: Hadoop 3.2.0 Reconstruction

大数据取证：Hadoop 3.2.0重构

• DOI: 10.1016/j.fsidi.2020.300909
• 发表时间: 2020
• 期刊: Forensic Science International: Digital Investigation
• 作者: Harshany, Edward;Benton, Ryan;Bourrie, David;Glisson, William
• 通讯作者: Glisson, William

Insight from a Containerized Kubernetes Workload Introspection

容器化 Kubernetes 工作负载自省的见解

• 发表时间: 2021
• 期刊: Proceedings of the 54th Hawaii International Conference on System Sciences
• 作者: Watts, Thomas Benton

Insight from a Docker Container Introspection

• DOI: 10.24251/hicss.2019.863
• 发表时间: 2019-01
• 作者: Thomas Watts;Ryan G. Benton;W. Glisson;Jordan Shropshire

DFS3: automated distributed file system storage state reconstruction

DFS3：自动化分布式文件系统存储状态重建

• DOI: 10.1145/3407023.3407056
• 发表时间: 2020
• 期刊: Reliability and Security
• 作者: Harshany, Edward;Benton, Ryan;Bourrie, David;Black, Michael;Glisson, William
• 通讯作者: Glisson, William