现代电子技术2025，Vol.48Issue(23)：89-96,8.DOI:10.16652/j.issn.1004-373x.2025.23.013
融合深度学习框架的通信安全态势感知技术
Communication security situation awareness technology integrating deep learning framework
摘要
Abstract
In view of the poor real-time performance,low accuracy,and insufficient robustness in current communication network security situational awareness technology solutions,this paper proposes a security situational awareness and prediction method based on a fusion architecture of improved convolutional neural network and long short-term memory network(CNN-LSTM).In this method,a multi-dimensional channel parameter real-time acquisition system is established to obtain key indicators such as signal-to-noise ratio(SNR),bit error rate(BER),response delay,and packet loss rate.Then,an adaptive feature extraction mechanism is used for preprocessing to improve the spatial feature extraction of CNN and capture temporal dependencies by combining with LSTM.The attention mechanism is introduced to optimize feature weight allocation.A dynamic threshold adjustment strategy based on deep reinforcement learning is proposed to effectively improve the adaptive ability of anomaly detection.A layered and cascaded security policy influence framework is used to achieve full link coverage from bottom layer parameter monitoring to top layer situational assessment.The results of the tests on public datasets and in actual communication network environment show that the accuracy rate of security situation recognition of the proposed method reaches 97.8%,its false alarm rate is reduced to 0.83%,and its response time is shortened to 23.5 ms.In conclusion,the comprehensive performance of the proposed method is improved significantly in comparison with that of the current mainstream methods,and it has good robustness,and can achieve efficient and accurate perception and rapid response to communication network security situations.关键词
深度学习/通信安全/态势感知/CNN-LSTM/异常检测/注意力机制/动态阈值Key words
deep learning/communication security/situation awareness/CNN-LSTM/anomaly detection/attention mechanism/dynamic threshold分类
电子信息工程引用本文复制引用
胡荣..融合深度学习框架的通信安全态势感知技术[J].现代电子技术,2025,48(23):89-96,8.基金项目
江西省教育厅科学技术研究项目(GJJ2407706) （GJJ2407706）
