DroidRL: Reinforcement Learning Driven Feature Selection for Android Malware Detection. (arXiv:2203.02719v2 [cs.CR] UPDATED)

Due to the completely open-source nature of Android, the exploitable
vulnerability of malware attacks is increasing. Machine learning, leading to a
great evolution in Android malware detection in recent years, is typically
applied in the classification phase. Since the correlation between features is
ignored in some traditional ranking-based feature selection algorithms,
applying wrapper-based feature selection models is a topic worth investigating.
Though considering the correlation between features, wrapper-based approaches
are time-consuming for exploring all possible valid feature subsets when
processing …

algorithms android android malware attacks classification correlation detection features great machine machine learning malware malware attacks malware detection nature vulnerability wrapper