提出一种基于深度学习的智能电视网络安全实时监测系统.该系统通过融合卷积神经网络(Convolutional Neural Networks,CNN)和长短时记忆网络(Long Short-Term Memory,LSTM)构建的HybridNet模型,对网络流量进行特征提取与威胁分类.结合自适应数据清洗(Adaptive Data Cleaning,ADC)算法和改进的孤立森林算法,该系统实现了对网络威胁的实时监测与快速响应.实验结果显示,该系统能够有效提高威胁检测的准确率和响应速度,并有效减少资源消耗.
Research on Real Time Monitoring of Intelligent Television Network Security Based on Deep Learning
This paper proposes a real-time security monitoring system for smart television networks based on deep learning.The HybridNet model constructed by Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),It is used for feature extraction and threat classification of network traffic.Combined with Adaptive Data Cleaning(ADC)algorithm and improved isolated forest algorithm,the system realizes real-time monitoring and rapid response to network threats.The experimental results show that the system can effectively improve the accuracy and response speed of threat detection,and effectively reduce resource consumption.
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