船舶网络入侵风险等级估算研究

Research on risk grade estimation of ship network intrusion

传统船舶网络入侵风险等级估算方法,存在风险数据局部搜索时间过长、风险等级估算准确性较低等弊端。为解决上述问题,建立基于BP神经网络的新型船舶入侵风险等级估算方法。通过网络结构的确定、初始值及阈值的选取,完成BP船舶神经网络的搭建。通过入侵数据采集、数据风险等级分类、风险度量评估,完成新型船舶网络入侵风险等级估算方法的搭建。设计对比实验结果表明,新型方法与传统方法相比,适当缩短风险数据局部搜索时间,并提升风险等级的估算准确性。

The traditional ship network intrusion risk grade estimation method has the disadvantages of too long local search time of risk data and low accuracy of risk grade estimation. In order to solve the above problems, a new method for estimating the risk level of network intrusion based on BP neural network is established. Through the determination of network structure, the selection of initial value and threshold, the construction of BP ship neural network is completed. Through the intrusion data collection, the classification of data risk grade, and the evaluation of risk measurement, a new method for estimating the risk level of the new ship network intrusion is built. Design comparison experiments show that the new method can shorten the local search time of risk data and improve the accuracy of risk level compared with traditional methods.