What is it about?
It is important to detect Android malware since it could expose a great threat to the users. In machine learning intelligence identification, too many insignificant features reduce accuracy. Thus, machine learning detection requires finding the important features quickly. This study offers the Pearson correlation coefficient (PMCC), which quantifies the linear relationship between all features. This study then uses a heatmap to visualize the PMCC value in heat version color.
Featured Image
Photo by Denny Müller on Unsplash
Why is it important?
Our experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA).
Perspectives
I hope this article will inspire additional research in the future as a way to combat the threat posed by malicious software. This article offers a high-level overview of the subject matter, and its primary purpose is to emphasize how essential to deepen our understanding of malware affecting Android devices.
Nur Khairani Kamarudin
Universiti Teknologi MARA
Read the Original
This page is a summary of: Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA), Journal of Intelligent & Fuzzy Systems, April 2023, IOS Press,
DOI: 10.3233/jifs-222612.
You can read the full text:
Resources
Contributors
The following have contributed to this page
