Anomalies Detection in Software by Conceptual Learning From Normal Executions

Anomalies Detection in Software by Conceptual Learning From Normal Executions

<p>Could 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 d...

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Bibliographic Details
Main Author: Ahmad Qadeib Alban (16855206) (author)
Other Authors: Fahad Islam (16870014) (author), Qutaibah M. Malluhi (14151912) (author), Ali Jaoua (16870017) (author)
Published: 2020
Subjects:
Information and computing sciences
Data management and data science
Machine learning
Software engineering
Anomaly detection
Software
Formal concept analysis
Feature extraction
Functional dependencies
Formal concept analysis (FCA)
Data reduction
Pattern generation
Functional dependencies preservation
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