  Grid computing systems require innovative methods and tools to identify
cybersecurity incidents and perform autonomous actions i.e. without
administrator intervention. They also require methods to isolate and trace job
payload activity in order to protect users and find evidence of malicious
behavior. We introduce an integrated approach of security monitoring via
Security by Isolation with Linux Containers and Deep Learning methods for the
analysis of real time data in Grid jobs running inside virtualized
High-Throughput Computing infrastructure in order to detect and prevent
intrusions. A dataset for malware detection in Grid computing is described. We
show in addition the utilization of generative methods with Recurrent Neural
Networks to improve the collected dataset. We present Arhuaco, a prototype
implementation of the proposed methods. We empirically study the performance of
our technique. The results show that Arhuaco outperforms other methods used in
Intrusion Detection Systems for Grid Computing. The study is carried out in the
ALICE Collaboration Grid, part of the Worldwide LHC Computing Grid.

Distributed, Parallel, and Cluster Computing;Cryptography and Security;Machine Learning