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The Android operating system is pervasively adopted as the operating systemplatform of choice for smart devices. However, the strong adoption has alsoresulted in exponential growth in the number of Android based malicioussoftware or malware. To deal with such cyber threats as part of cyberinvestigation and digital forensics, computational techniques in the form ofmachine learning algorithms are applied for such malware identification,detection and forensics analysis. However, such Computational Forensicsmodelling techniques are constrained the volume, velocity, variety and veracityof the malware landscape. This in turn would affect its identification anddetection effectiveness. Such consequence would inherently induce the questionof sustainability with such solution approach. One approach to optimiseeffectiveness is to apply dimensional reduction techniques like PrincipalComponent Analysis with the intent to enhance algorithmic performance. In thispaper, we evaluate the effectiveness of the application of Principle ComponentAnalysis on Computational Forensics task of detecting Android based malware. Weapplied our research hypothesis to three different datasets with differentmachine learning algorithms. Our research result showed that the dimensionallyreduced dataset would result in a measure of degradation in accuracyperformance.

A Review on the effectiveness of Dimensional Reduction with Computational Forensics: An Application on Malware Analysis