基于深度学习的网络安全威胁检测与防御策略研究OA

Deep learning technology,with its powerful data processing capability and feature extraction mechanism,brings new solutions to the field of network security.The applications of deep learning in cybersecurity threat detection and defense include malware detection,attack behavior identification and network traffic analysis using convolutional neural networks(CNN)and recurrent neural networks(RNN).Also,the potential and challenges of deep learning models for insider threat detection are explored,such as the high dimensionality and complexity of data and the impact of adversarial attacks.An intelligent network security protection strategy based on Deep Reinforcement Learning(DRL)is presented,which learns by interacting with the environment to obtain an optimal security protection strategy.Despite the promising application of deep learning in cyber security,there are still problems such as insufficient data,poor model interpretability and insufficient robustness.