Bearicade: Secure access gateway to High Performance Computing systems

Taha Al-Jody, Violeta Holmes, Alexandros Antoniades, Yazan Kazkouzeh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
EditorsGuojun Wang, Ryan Ko, Md Zakirul Alam Bhuiyan, Yi Pan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1420-1427
Number of pages8
ISBN (Electronic)9781665403924
ISBN (Print)9781665403931
DOIs
Publication statusPublished - 9 Feb 2021
Event19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications - Guangzhou, China
Duration: 29 Dec 20201 Jan 2021
Conference number: 19

Publication series

NameProceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020
PublisherIEEE
ISSN (Print)2324-898X
ISSN (Electronic)2324-9013

Conference

Conference19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Abbreviated titleTrustCom 2020
CountryChina
CityGuangzhou
Period29/12/201/01/21

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