{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/arhuaco-deep-learning-and-isolation-based","title":"Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing","arxiv_id":"1801.04179","date":"2018-01-12","proceeding":null,"authors":["A. Gomez Ramirez","C. Lara","L. Betev","D. Bilanovic","U. Kebschull","for the ALICE Collaboration"],"abstract":"Grid computing systems require innovative methods and tools to identify\ncybersecurity incidents and perform autonomous actions i.e. without\nadministrator intervention. They also require methods to isolate and trace job\npayload activity in order to protect users and find evidence of malicious\nbehavior. We introduce an integrated approach of security monitoring via\nSecurity by Isolation with Linux Containers and Deep Learning methods for the\nanalysis of real time data in Grid jobs running inside virtualized\nHigh-Throughput Computing infrastructure in order to detect and prevent\nintrusions. A dataset for malware detection in Grid computing is described. We\nshow in addition the utilization of generative methods with Recurrent Neural\nNetworks to improve the collected dataset. We present Arhuaco, a prototype\nimplementation of the proposed methods. We empirically study the performance of\nour technique. The results show that Arhuaco outperforms other methods used in\nIntrusion Detection Systems for Grid Computing. The study is carried out in the\nALICE Collaboration Grid, part of the Worldwide LHC Computing Grid.","url_abs":"http://arxiv.org/abs/1801.04179v1","url_pdf":"http://arxiv.org/pdf/1801.04179v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"arhuaco-deep-learning-and-isolation-based","repo_url":"https://github.com/kuronosec/arhuaco","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"intrusion-detection","task_name":"Intrusion Detection"},{"task_slug":"malware-detection","task_name":"Malware Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}