This paper applied reinforcement learning algorithms to the feature selection phase of Android malware detection, reducing the burden of feature selection tasks ...

Mar 5, 2022 · The framework deploys DDQN algorithm to obtain a subset of features which can be used for effective malware classification.

Mar 26, 2022 · The main idea is to apply Double DQN(DDQN) algorithm to select a subset of features that can be effectively used for malware classification. To ...

This paper applied reinforcement learning algorithms to the feature selection phase of Android malware detection, reducing the burden of feature selection tasks ...

Feature Selection for Malware Detection Based on Reinforcement Learning · Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and ...

In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement learning. Firstly, we extract sensitive ...

This paper proposes an Android malware detection framework based on sensitive opcode fragments based on sensitive elements and deep reinforcement learning, ...

There is a real need of dimension reduction approach of malware features for better detection. This system describes for malware detection and characterization ...

The authors of [35] developed DroidRL, a feature selection approach for Android malware detection using reinforcement learning. The study explores the use of ...

DroidRL: Feature selection for android malware detection with reinforcement learning ... Bibi, A dynamic DL-driven architecture to combat sophisticated ...