摘要
在数字化和智能化发展过程中,广播电视网络面临诸如分布式拒绝服务(Distributed Denial of Service,DDoS)攻击、信号劫持、恶意代码植入等多种安全威胁。传统的安全防御方法在实时检测和精准识别方面存在局限性,难以应对复杂的网络攻击模式。针对这一问题,提出一种基于深度学习的安全威胁检测与防御框架,该框架结合卷积神经网络(Convolutional Neural Network,CNN)、循环神经网络(Recurrent Neural Network,RNN)和长短时记忆(Long Short-Term Memory,LSTM)网络等模型优化特征提取与分类策略,并引入联邦学习技术实现跨节点协同防御,旨在提升广播电视网络对各种安全威胁的识别与防护能力。实验结果验证了所提框架在提高威胁检测精度、实时性和系统稳定性方面的有效性,为广播电视网络的安全防护提供了有力的技术支持。
In the process of digital and intelligent development,broadcast and television networks are faced with multiple security threats such as Distributed Denial of Service(DDoS)attacks,signal hijacking,malicious code implantation and so on.However,traditional security defense methods have limitations in real-time detection and accurate identification.It is difficult to cope with complex network attack patterns.To solve this problem,a security threat detection and defense framework based on deep learning is proposed,which combines Convolutional Neural Network(CNN),Recurrent Neural Network(RNN)and Long Short-Term Memory(LSTM)models optimize feature extraction and classification strategies,and introduce federated learning technology to achieve cross-node collaborative defense.It aims to improve the identification and protection ability of radio and television networks to various security threats.The experimental results verify the effectiveness of the proposed framework in improving the accuracy,real-time performance and system stability of threat detection,and provide strong technical support for the security protection of broadcast and television networks.
作者
李磊
LI Lei(Shanxi Radio and Television Station,Taiyuan 030001,China)
出处
《电视技术》
2025年第6期187-189,共3页
Video Engineering
关键词
广播电视网络安全
深度学习
异常检测
智能分类
broadcasting and television network security
deep learning
anomaly detection
