Abstract: Cloud-based malware detection improves the detection performance for mobile devices that offload their malware detection tasks to security servers ...

In Android malware defenses, DRL is also a potentially useful tool to improve the effectiveness, generalization, and interpretability of defense strategies. Wan ...

This paper investigates the competition of the radio transmission bandwidths and the data sharing of the security server in the dynamic malware detection ...

In this paper, we investigate the competition of the radio transmission bandwidths and the data sharing of the security server in the dynamic malware detection ...

Dec 7, 2017 · Malware Detection Methods. Signature-based detection. Rely on human expertise in creating the label of malicious behaviors.

An offloading algorithm based on Q-learning is proposed for smartphones to determine their offloading rates for malware detection with unknown parameters such ...

Nov 23, 2016 · #8239; Reinforcement learning based cloud malware detection. #8239 ... #8239; Q-learning based malware detection: Offloading rate is chosen.

This paper designs a malware detection scheme with Q-learning for a mobile device to derive the optimal offloading rate without knowing the trace generation ...

AutoPentest-DRL is an automated penetration testing framework based on Deep Reinforcement Learning (DRL) techniques. AutoPentest-DRL can determine the most ...

In this paper, we investigate the cloud-based malware detection game, in which mobile devices offload their application traces to security servers via base ...