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.

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中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，应该能够更好地控制网络安全，降低网络安全漏洞的风险。这些增强功能的可用性也将允许使用这些系统进行应用，跨学科的研究，使用大规模的数据和预测建模的互相关分析。调查人员系统地衡量性能，以支持对数据残留的法医分析，从而发现使用此类平台可能存在的安全风险。仪器的采购产生了数据挖掘，安全和取证研究的显着扩展。网络安全、数字取证和数据挖掘研究的核心研究重点是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制定工作计划。获得的工具和专业知识为该机构提供了支持国家级客户的能力，例如美国陆军航空导弹研究，开发和工程中心，国家卫生研究院（NIH）以及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一种工具，以便调查人员能够-调整、调整和重新部署环境，-使节点离线以进行取证检查，-确保存在一致的基线，以与其他环境进行比较，在其学科范围内进行有意义的实验，同时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）。拟议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的培训和接触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。获得的技能应直接导致实习和长期就业机会的博士生在六个学院和/或研究所。该大学服务于农村和经济贫困地区的比例很高;非白人学生的入学率为34%，女性入学率为60%。该仪器提供了接触机会，队列活动的奖学金服务计划。

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