DroidOL: Those who watch Internet security news will know that criminals are becoming more and more inventive, developing new tools, and discovering new ways of attacking to go unnoticed by conventional security solutions. A team of researchers from Nanyang University of Technology, Singapore, created a new large-scale solution for detecting malware on Android.
It is called DroidOL, and is a customized and extensible malware detection framework based on online learning.
Let's see how the DroidOL framework helps improve the detection of Android malware.
"DroidOL achieves superior accuracy by extracting high quality features from inter-procedural control-flow graphs (ICFGs) of applications, which are known to be very powerful during the various obscuration techniques used. are used by malware ", the researchers explain.
The researchers used the Weisfeiler-Lehman (WL) graph kernel to extract semantic features from the ICFGs, and finally e-learning to distinguish between good and bad applications.
The model is continually retrained, and ultimately, it performs significantly better than the engineering-based learning techniques that dominate various platforms (including Android OS).
“In a large-scale benchmarking with more than 87.000 applications, DroidOL achieves an accuracy of 84.29%, surpassing two state-of-the-art malware techniques by more than 20% in a standard learning environment and over 3% when is constantly being trained, ”the researchers note.
More details about DroidOL can be found at the following link:
George is still wondering what he is doing here….
