CICI: RSARC: Infrastructure Support for Securing Large-Scale Scientific Workflows

CICI：RSARC：确保大规模科学工作流程安全的基础设施支持

• 批准号: 1738929
• 负责人: Ping Yang
• 金额: $ 100万
• 依托单位: SUNY at Binghamton
• 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: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738929&HistoricalAwards=false
• 关键词: CICI; RSARC; Infrastructure; Support; Securing

The scientific workflow is an important paradigm for automating and accelerating data processing and sharing in the scientific community. The correctness of scientific discoveries relies on the trustworthiness and reliability of the data processed by scientific workflows and the underlying cyberinfrastructure. Unfortunately, modern scientific workflow systems lack robust infrastructure support for the trustworthy execution of scientific workflows and for the protection of the data processed by such workflows. A scientist or student may forge or alter datasets or computation simply to get papers accepted for publication. A malicious user may also publish forged workflow data on websites, misleading other scientists into investigating and publishing invalid results. This project aims to support a community of engineers and scientists to collaboratively and securely collect, analyze, and share data using scientific workflows. The success of this project contributes significantly to the national cyberinfrastructure vision of securing the scientific discovery process for a wide range of science and engineering disciplines.This project develops infrastructure support for secure execution of scientific workflows, detection of anomalous execution flows, and protection of scientific data. In particular, this project: (1) develops a trusted execution environment for scientific workflows leveraging the Intel Software Guard Extension (SGX) to protect the execution of scientific workflows as well as the data processed by scientific workflows; (2) produces encrypted, tamper-proof, and non-repudiable block-graphs that enable scientists to verify the origin of scientific data and examine how a piece of data was modified and distributed; and (3) develops a machine-learning based anomaly detection technique to detect anomalous execution flows based on logs collected by the underlying cyberinfrastructure.

科学工作流是科学界自动化和加速数据处理和共享的重要范例。科学发现的正确性取决于科学工作流程和基础网络基础设施处理的数据的可信度和可靠性。不幸的是，现代科学工作流系统缺乏强大的基础设施支持，以可靠地执行科学工作流，并保护这些工作流处理的数据。科学家或学生可能伪造或修改数据集或计算，只是为了让论文被接受发表。恶意用户还可能在网站上发布伪造的工作流数据，误导其他科学家进行调查并发布无效结果。该项目旨在支持一个由工程师和科学家组成的社区，使用科学工作流程协作、安全地收集、分析和共享数据。 该项目的成功极大地促进了国家网络基础设施的愿景，即确保广泛的科学和工程学科的科学发现过程。该项目为科学工作流程的安全执行、异常执行流的检测和科学数据的保护提供基础设施支持。 具体而言，该项目：（1）利用英特尔Software Guard Extension（SGX）为科学工作流开发可信执行环境，以保护科学工作流的执行以及科学工作流处理的数据;（2）生成加密、防篡改和不可否认的块图，使科学家能够验证科学数据的来源，并检查数据是如何被修改和分发的;以及（3）开发基于机器学习的异常检测技术，以基于由底层网络基础设施收集的日志来检测异常执行流。

项目成果

Deep-Learning-as-a-Workflow (DLaaW): An Innovative Approach to Enabling Deep Learning in Scientific Workflows

深度学习作为工作流程 (DLaaW)：在科学工作流程中启用深度学习的创新方法

• DOI: 10.1109/bigdata52589.2021.9671626
• 发表时间: 2021
• 期刊: IEEE International Conference on Big Data
• 作者: Liu, Junwen;Xiao, Ziyun;Lu, Shiyong;Che, Dunren
• 通讯作者: Che, Dunren

A Security Framework for Scientific Workflow Provenance Access Control Policies

• DOI: 10.1109/tsc.2019.2921586
• 发表时间: 2022-01-01
• 期刊: IEEE TRANSACTIONS ON SERVICES COMPUTING
• 影响因子: 8.1
• 作者: Bhuyan, Fahima Amin;Lu, Shiyong;Ahmed, Ishtiaq
• 通讯作者: Ahmed, Ishtiaq

Preliminary Study of Trusted Execution Environments on Heterogeneous Edge Platforms

• DOI: 10.1109/sec.2018.00057
• 发表时间: 2018-10
• 期刊: 2018 IEEE/ACM Symposium on Edge Computing (SEC)
• 作者: Zhenyu Ning;Jinghui Liao;Fengwei Zhang;Weisong Shi

SEED: Confidential Big Data Workflow Scheduling with Intel SGX Under Deadline Constraints

• DOI: 10.1109/scc49832.2020.00023
• 发表时间: 2020-11
• 期刊: 2020 IEEE International Conference on Services Computing (SCC)
• 作者: Ishtiaq Ahmed;S. Mofrad;Shiyong Lu;Changxin Bai;Fengwei Zhang;D. Che

Real-Time Evasion Attacks against Deep Learning-Based Anomaly Detection from Distributed System Logs

• DOI: 10.1145/3422337.3447833
• 发表时间: 2021-04
• 期刊: Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy
• 作者: J. D. Herath;Ping Yang;Guanhua Yan