Show simple item record

AuthorAlban, Ahmad Qadeib
AuthorIslam, Fahad
AuthorMalluhi, Qutaibah M.
AuthorJaoua, Ali
Available date2024-07-17T07:14:45Z
Publication Date2020
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2020.3027508
ISSN21693536
URIhttp://hdl.handle.net/10576/56753
AbstractCould we detect anomalies during the run-time of a program by learning from the analysis of its previous traces for normally completed executions? In this paper we create a featured data set from program traces at run time, either during its regular life, or during its testing phase. This data set represents execution traces of relevant variables including inputs, outputs, intermediate variables, and invariant checks. During a learning mining step, we start from exhaustive random training input sets and map program traces to a minimal set of conceptual patterns. We employ formal concept analysis to do this in an incremental way, and without losing dependencies between data set features. This set of patterns becomes a reference for checking the normality of future program executions as it captures invariant functional dependencies between the variables that need to be preserved during execution. During the learning step, we consider enough input classes corresponding to the different patterns by using random input selection until reaching stability of the set of patterns (i.e. the set is almost no longer changing, and only negligible new patterns are not reducible to it). Experimental results show that the generated patterns are significant in representing normal program executions. They also enable the detection of different executable code contamination at early stages. The proposed method is general and modular. If applied systematically, it enhances software resilience against abnormal and unpredictable events.
SponsorThis work was supported by the Qatar National Research Fund (Member of the Qatar Foundation) under Grant NPRP X-063-1-014. Open Access funding was provided by the Qatar National Library.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAnomaly detection; Data reduction; Formal concept analysis (FCA); Functional dependencies; Functional dependencies preservation; Pattern generation
TitleAnomalies detection in software by conceptual learning from normal executions
TypeArticle
Pagination179845-179856
Volume Number8
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record