Classification of Android Applications and Performance Evaluation of Machine Learning Model with HFST
Keywords:
Machine learning algorithms, Hybrid features, Classification and detection, Performance evaluationAbstract
In the dynamic landscape of mobile applications, Android devices seamlessly integrate into our daily lives, offering diverse functionalities through various applications. However, the surge in malware and malicious software poses a significant challenge for security professionals and users alike. To address this issue, researchers and cyber security experts actively explore innovative methods. This research paper delves into the crucial domain of Android application categorization, evaluating the performance of a novel machine learning model incorporating a Hybrid Feature Selection Technique (HFST). Initially, the study identifies the top twenty significant features through information gain, feature selection, and chi-square methods. The classifier's performance is then assessed using these features. Subsequently, all 60 features selected through the three techniques are merged and out of the 60 features, 11 are identified as hybrid features that are common in at least two techniques. The study re-evaluates machine learning classifiers' performance using these 11 hybrid features, comparing the results with existing state-of-the-art techniques to illustrate the superiority of the proposed HFST-based technique. Furthermore, the study measures its impact on various performance metrics, including classification accuracy, precision, and f-measure, revealing notable enhancements across these parameters when employing the HFST. The application of this hybrid feature selection technique significantly improves the classification process, ultimately achieving an impressive classification accuracy of 98.11%, with precision at 97.56 and an f-measure of 97.99 for distinguishing between malicious and benign apps.
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