摘要
针对广泛应用于医院的软件定义网络(SDN)难以应对内部网络威胁的问题,设计了一种基于贝叶斯推理的信任管理机制来识别网络内部可能存在的恶意设备。该机制主要是利用贝叶斯推理方法推导出恶意攻击数据包的发送概率从而实现对网络内部设备的信任管理。通过仿真环境和真实网络环境下的实验证明了该方法可以比类似的方法更快地降低恶意设备的信任值。
A software-defined network(SDN)widely used in hospitals is difficult to deal with internal network threats.A Bayesian-based trust management mechanism is designed to identify possible malicious devices inside the network.The mechanism mainly uses the Bayesian inference method to derive the probability of sending malicious attack packets to realize the trust management of the internal devices of the network.Experiments in the simulation environment and the real network environment prove that the method can reduce the trust value of malicious devices faster than similar methods..
作者
郭天伟
杨海东
GUO Tian-wei;YANG Hai-dong(Information Statistics Center,Huai'an Second People's Hospital,Huai’an,Jiangsu 223001,China;School of Computer Science and Technology,Huaiyin Normal University,Huai’an,Jiangsu 223001,China)
出处
《计算技术与自动化》
2020年第4期180-184,共5页
Computing Technology and Automation
关键词
入侵检测
软件定义网络
医疗网络
贝叶斯推理
intrusion detection
software-defined network
medical network
Bayesian inference
