Continuous Learning for Android Malware Detection. (arXiv:2302.04332v1 [cs.CR])

Machine learning methods can detect Android malware with very high accuracy.
However, these classifiers have an Achilles heel, concept drift: they rapidly
become out of date and ineffective, due to the evolution of malware apps and
benign apps. Our research finds that, after training an Android malware
classifier on one year's worth of data, the F1 score quickly dropped from 0.99
to 0.76 after 6 months of deployment on new test samples.

In this paper, we propose new methods to …

accuracy achilles android android malware apps concept continuous data deployment detect detection high machine machine learning malware malware detection research score test training