Intrusion Prevention and Detection in Grid Computing - The ALICE Case

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Gomez, Andres^1 ; Lara, Camilo^1 ; Kebschull, Udo^1

Johann-Wolfgang-Goethe University, Frankfurt, Germany^1

关键词: Computational resources;    European organization for nuclear researches;    Integrated solutions;    Intrusion prevention;    Large Hadron collider LHC;    Machine learning approaches;    Security challenges;    Zero day vulnerabilities;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/6/062017/pdf
DOI  :  10.1088/1742-6596/664/6/062017

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can execute arbitrary code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.