光纤网络入侵中的未感染节点检测

光纤网络采用开放性较强的分布式结构，易受到恶意数据和代码的入侵，提出基于多元节点属性分类的光纤网络入侵未感染节点检测算法研究。依据节点测距原理，提取光纤网络中全部节点的位置信息；选定与未感染节点类型相关的光纤节点特征属性，并针对节点属性和入侵类型建模；依据多元分类算法对提取的光纤节点样本空间采样特征数据进行学习和分类，识别出光纤网络中的未感染节点。仿真实验表明，提出的节点检测算法克服了传统算法的弊端和不足，能够有效降低通信成本和节点能耗、提高入侵检测率、延长光纤网络生命周期。

Non-infected node detection in fiber optic network intrusion

Open the stronger distributed structure is used in optical network vulnerable to malicious invasion of data and code, is proposed based on multiple nodes attribute classification of fiber optic network intrusion uninfected node detection algorithm research. According to the principle of node ranging, the position information of all nodes in fiber optic network is extracted. Select the fiber node feature attributes associated with the uninfected node type and model the node properties and intrusion types; According to the multivariate classification algorithm, the data of sampling spatial sampling data of optical fiber nodes was studied and classified, and the healthy nodes in the fiber network were identified. Simulation results show that the proposed node detection algorithm to overcome the shortcomings and the insufficiency of traditional algorithm, can effectively reduce the communication cost and node energy consumption, improve intrusion detection rate, extend the fiber optic network life cycle.