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.
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).
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,
You can read the full text:
The following have contributed to this page