Intelligent Security Detection and Defense in Operating Systems Based on Deep Learning

With the development of network technology, APT(advanced persistentthreat) attacks are increasing, and research on the security of enterprise assets requires effective detection of digital assets in the network space and effective management of assets through screening and combing, which is the key to real-time monitoring of the safe operation of the system. However, the accompanying malware also poses a threat to the user's property and privacy, so an effective method of detecting Android malware is necessary. In this research direction, although the feature processing capability of traditional machine learning has been improved, there are problems that feature extraction relies on expert experience and the accuracy is low. Therefore, this paper combines deep learning reinforcement technology with the operating system to detect and defend the system in advance, so as to achieve network security.