基于深度学习的SDN恶意应用的检测方法

Detection of malicious SDN application based on deep learning

SDN(软件定义网络)核心技术是通过将网络设备控制层与数据转发层分离,实现网络流量的灵活控制。目前针对SDN网络架构的恶意应用研究还较少。针对这一问题,在总结分析现有恶意应用检测方法的基础上,基于深度学习技术提出一种面向SDN恶意应用的检测方法,将恶意应用转化为图片,在TensorFlow深度学习框架下对32个SDN恶意样本进行学习和检测,实验数据表明,该方法对恶意应用检测率可以达到89%,验证了方案的可行性。

The core technology SDN (software defined network) is to realize the flexible control of network traffic by separating the network device control layer from the data forwarding layer.At present,there are few researches on malicious application of SDN network architecture.To solve this problem,based on the analysis of existing malicious application detection methods,a detection method for SDN malicious application was proposed based on deep learning technology.The malicious application was transformed into a picture,and 32 SDN malicious samples were learned and detected under the TensorFlow depth learning framework.Experimental data show that the detection rate of malicious application can reach 89%,which verifies the feasibility of the proposed scheme.