基于卷积神经网络的移动通信网络入侵检测方法研究

Study on the intrusion detection method of mobile communication network based on convolutional neural network

传统的入侵检测方法多基于规则匹配或统计分析,能够识别已知的攻击模式,但对于未知的新型攻击或变种攻击时,检测效果不佳,因此,文章提出基于卷积神经网络的移动通信网络入侵检测方法研究。实验结果表明,该设计方法在移动通信网络入侵检测中取得了显著成效,误报率仅为0.87%,漏报率仅为1.36%,证明卷积神经网络在移动通信网络入侵检测领域具有较高的准确性和稳定性。

Traditional intrusion detection methods are mostly based on rule matching or statistical analysis,which can identify known attack patterns.However,the detection effect is not good for unknown new or variant attacks.Therefore,a mobile communication network intrusion detection method based on convolutional neural networks is proposed for research.The experimental results show that the design method has achieved significant results in mobile communication network intrusion detection,with a false alarm rate of only 0.87%and a false alarm rate of only 1.36%,proving that convolutional neural networks have high accuracy and stability in the field of mobile communication network intrusion detection.