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Grid computing systems require innovative methods and tools to identifycybersecurity incidents and perform autonomous actions i.e. withoutadministrator intervention. They also require methods to isolate and trace jobpayload activity in order to protect users and find evidence of maliciousbehavior. We introduce an integrated approach of security monitoring viaSecurity by Isolation with Linux Containers and Deep Learning methods for theanalysis of real time data in Grid jobs running inside virtualizedHigh-Throughput Computing infrastructure in order to detect and preventintrusions. A dataset for malware detection in Grid computing is described. Weshow in addition the utilization of generative methods with Recurrent NeuralNetworks to improve the collected dataset. We present Arhuaco, a prototypeimplementation of the proposed methods. We empirically study the performance ofour technique. The results show that Arhuaco outperforms other methods used inIntrusion Detection Systems for Grid Computing. The study is carried out in theALICE Collaboration Grid, part of the Worldwide LHC Computing Grid.

Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing