A Review on the effectiveness of Dimensional Reduction with Computational Forensics: An Application on Malware Analysis
This work addresses the challenge of handling large-scale malware data for cybersecurity investigators, but it is incremental as it tests a standard technique with negative results.
The paper evaluated the effectiveness of Principal Component Analysis (PCA) for dimensionality reduction in computational forensics for Android malware detection, finding that it led to a degradation in accuracy performance across three datasets with different machine learning algorithms.
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.