Towards Automatic Application Fingerprinting Using Performance Monitoring CountersShow others and affiliations
2021 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2021Conference paper, Published paper (Refereed)
Abstract [en]
In this paper, we discuss a method for application fingerprinting using conventional hardware and software performance counters. Modern applications are complex and often utilizes a broad spectra of the available hardware resources, where multiple performance counters can be of significant interest. The number of performance counters that can be captured simultaneously is, however, small due to hardware limitations in most modern computers. We propose to mitigate the hardware limitations using an intelligent mechanism that pinpoints the most relevant performance counters for an application's performance. In our proposal, we utilize the Pearson correlation coefficient to rank the most relevant PMU events and filter out events of less relevance to an application's execution. Our ultimate goal is to establish a comparable application fingerprint model using performance counters, that we can use to classify applications. The classification procedure can then be used to determine the type of application's fingerprint, such as malicious software.
Place, publisher, year, edition, pages
Association for Computing Machinery , 2021.
Keywords [en]
Computer hardware, Correlation methods, Application fingerprinting, Automatic application, Classification procedure, Hardware and software, Intelligent mechanisms, Pearson correlation coefficients, Performance counters, Performance monitoring, Application programs
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-58797DOI: 10.1145/3459960.3461557Scopus ID: 2-s2.0-85107211777ISBN: 9781450390576 (print)OAI: oai:DiVA.org:mdh-58797DiVA, id: diva2:1683002
Conference
7th Conference on the Engineering of Computer Based Systems, ECBS 2021, 26 May 2021 through 27 May 2021
Note
Conference code: 169185; Export Date: 8 June 2022; Conference Paper
2022-07-132022-07-132022-11-08Bibliographically approved