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DTSTAMP:20250626T234543Z
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DTSTART;TZID=America/New_York:20241117T120000
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UID:submissions.supercomputing.org_SC24_sess733_ws_shpc103@linklings.com
SUMMARY:Using Malware Detection Techniques for HPC Application Classificat
 ion
DESCRIPTION:Thomas Jakobsche and Florina Ciorba (University of Basel, Swit
 zerland)\n\nHPC systems face security and compliance challenges, particula
 rly in preventing waste and misuse of computational resources by unauthori
 zed or malicious software that deviates from allocation purpose. Existing 
 methods to classify applications based on job names or resource usage are 
 often unreliable or fail to capture applications that have different behav
 ior due to different inputs or system noise. This research proposes an app
 roach that uses similarity-preserving fuzzy hashes to classify HPC applica
 tion executables. By comparing the similarity of SSDeep fuzzy hashes, a Ra
 ndom Forest Classifier can accurately label applications executing on HPC 
 systems including unknown samples. We evaluate the Fuzzy Hash Classifier o
 n a dataset of 92 application classes and 5333 distinct application sample
 s. The proposed method achieved a macro f1-score of 90% (micro f1-score: 8
 9%, weighted f1-score: 90%). Our approach addresses the critical need for 
 more effective application classification in HPC environments, minimizing 
 resource waste, and enhancing security and compliance.\n\nTag: Application
 s and Application Frameworks, Artificial Intelligence/Machine Learning, Se
 curity\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Nael
  Abu-Ghazaleh (University of California, Riverside); Kevin J. Barker (Paci
 fic Northwest National Laboratory (PNNL)); Yang Guo (National Institute of
  Standards and Technology (NIST)); Joseph Manzano (Pacific Northwest Natio
 nal Laboratory (PNNL)); Andres Marquez (Pacific Northwest National Laborat
 ory (PNNL)); and Sean Peisert (Lawrence Berkeley National Laboratory (LBNL
 ); University of California, Davis)\n\n
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