Bearicade:高性能计算系统的安全接入网关

Taha Al-Jody, Violeta Holmes, Alexandros Antoniades, Yazan Kazkouzeh
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引用次数: 1

摘要

网络安全正在成为许多信息技术和计算系统的重要组成部分。高性能计算系统越来越多地应用于科研、学术和工业领域。高性能计算应用程序是专门设计来利用高性能计算系统的并行特性的。目前对高性能计算系统的研究主要集中在软件开发、并行算法和计算机系统架构方面的改进。然而,在开发通用的高性能计算安全标准方面还没有做出重大努力。高性能计算资源的安全性通常是现有各种机构策略的附加项,这些策略没有考虑到高性能计算安全性的额外需求。此外,用户访问高性能计算资源的终端或门户往往不安全,或者处于未受保护的网络中。本文介绍了一个数据驱动的安全编排自动化和响应系统Bearicade。Bearicade从高性能计算系统及其用户收集数据,使用基于机器学习的解决方案来解决当前高性能计算系统中的安全问题。系统安全是通过监测、分析和解释用户活动、服务器请求、使用的设备和地理位置等数据来实现的。使用机器学习算法可以检测到用户行为中的任何异常,并且系统管理员可以看到这些异常,从而帮助调解威胁。系统在一个大学校园网格系统上进行了系统管理员和用户的测试。两个案例研究,用户行为异常检测和恶意Linux终端命令分类,展示了机器学习方法在识别潜在安全威胁方面的应用。Bearicade的数据被用于实验。结果表明,为HPC管理员提供了详细的信息,以便检测可能的安全攻击并及时采取行动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bearicade: secure access gateway to High Performance Computing systems
Cyber security is becoming a vital part of many information technologies and computing systems. Increasingly, High-Performance Computing systems are used in scientific research, academia and industry. High-Performance Computing applications are specifically designed to take advantage of the parallel nature of High-Performance Computing systems. Current research into High-Performance Computing systems focuses on the improvements in software development, parallel algorithms and computer systems architecture. However, there are no significant efforts in developing common High-Performance Computing security standards. Security of the High-Performance Computing resources is often an add-on to existing varied institutional policies that do not take into account additional requirements for High-Performance Computing security. Also, the users' terminals or portals used to access the High-Performance Computing resources are frequently insecure or they are being used in unprotected networks. In this paper we present Bearicade - a Data-driven Security Orchestration Automation and Response system. Bearicade collects data from the HPC systems and its users, enabling the use of Machine Learning based solutions to address current security issues in the High-Performance Computing systems. The system security is achieved through monitoring, analysis and interpretation of data such as users' activity, server requests, devices used and geographic locations. Any anomaly in users' behaviour is detected using machine learning algorithms, and would be visible to system administrators to help mediate the threats. The system was tested on a university campus grid system by administrators and users. Two case studies, Anomaly detection of user behaviour and Classification of Malicious Linux Terminal Command, have demonstrated machine learning approaches in identifying potential security threats. Bearicade's data was used in the experiments. The results demonstrated that detailed information is provided to the HPC administrators to detect possible security attacks and to act promptly.
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