DroidOL Learning Algorithm improves the detection of Android malware

: Όσοι παρακολουθούν τα νέα για την διαδικτυακή ασφάλεια θα γνωρίζουν ότι οι εγκληματίες γίνονται συνεχώς και πιο ευρηματικοί, αναπτύσσουν νέα , and are discovering new ways of attacking to go unnoticed by conventional solutions . Μια ομάδα ερευνητών από το Τεχνολογικό Nanyang, Singapore has created a new large-scale solution for Android malware detection.Android Malware Detection DroidOL

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 applications' inter-procedural control-flow graphs (ICFGs), which are known to appear very strongly during the various obfuscation techniques that are used by the malicious ”, the researchers explain.

The researchers used the Weisfeiler-Lehman (WL) graph kernel for Eq semantic features from 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:

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Written by giorgos

George still wonders what he's doing here ...

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