  The Android operating system is pervasively adopted as the operating system
platform of choice for smart devices. However, the strong adoption has also
resulted in exponential growth in the number of Android based malicious
software or malware. To deal with such cyber threats as part of cyber
investigation and digital forensics, computational techniques in the form of
machine learning algorithms are applied for such malware identification,
detection and forensics analysis. However, such Computational Forensics
modelling techniques are constrained the volume, velocity, variety and veracity
of the malware landscape. This in turn would affect its identification and
detection effectiveness. Such consequence would inherently induce the question
of sustainability with such solution approach. One approach to optimise
effectiveness is to apply dimensional reduction techniques like Principal
Component Analysis with the intent to enhance algorithmic performance. In this
paper, we evaluate the effectiveness of the application of Principle Component
Analysis on Computational Forensics task of detecting Android based malware. We
applied our research hypothesis to three different datasets with different
machine learning algorithms. Our research result showed that the dimensionally
reduced dataset would result in a measure of degradation in accuracy
performance.
