uPAD: Unsupervised Privacy-Aware Anomaly Detection in High Performance Computing Systems

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review



Rapid growing complexity of HPC systems in response to demand for higher computing performance, results in higher probability of failures. Early detection of failures significantly reduces the damages caused by failure via impeding their propagation through system. Various anomaly detection mechanism are proposed to detect failures in their early stages. Insufficient amount of failure samples in addition to privacy concerns extremely limits the functionality of available anomaly detection approaches. Advances in machine learning techniques, significantly increased the accuracy of unsupervised anomaly detection methods, addressing the challenge of insufficient failure samples. However, available approaches are either domain specific, inaccurate, or require comprehensive knowledge about the underlying system. Furthermore, processing certain monitoring data such as system logs raises high privacy concerns. In addition, noises in monitoring data severely impact the correctness of data analysis. This work proposes an unsupervised and privacy-aware approach for detecting abnormal behaviors in general HPC systems. Preliminary results indicate high potentials of autoencoders for automatic detection of abnormal behaviors in HPC systems via analyzing anonymized system logs using fast-trainable noise-resistant models.


Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Pattern Recognition Applications and Methods - ICPRAM
EditorsMaria De Marsico, Gabriella Sanniti di Baja, Ana L.N. Fred
PublisherSCITEPRESS - Science and Technology Publications
Number of pages8
ISBN (print)9789897583513
Publication statusPublished - 2019

Publication series

SeriesInternational Conference on Pattern Recognition Applications and Methods (ICPRAM)


Title8th International Conference on Pattern Recognition Applications and Methods
Abbreviated titleICPRAM 2019
Conference number8
Duration19 - 21 February 2019
Degree of recognitionInternational event
CountryCzech Republic

External IDs

Scopus 85064662094
WOS 000659174900098
Scopus 85174850627



  • Anomaly Detection, Neural Networks, Noise Mitigation, Pattern Detection, Time Series Analysis