【摘要】 针对网络安全态势感知中态势要素获取困难问题,给出一种基于粒子群优化的网络安全态势要素获取模型.在获取模型中,引入模糊技术对输入的历史态势要素集进行模糊化预处理后,转化为模糊逻辑规则,映射到神经网络层与层之间,以提高神经网络的学习能力.利用粒子群优化算法优化神经网络的连接权以提高神经网络的学习精度和速度.仿真实验结果表明,该模型是一种有效可行的态势要素提取技术,并具有较好的泛化能力.更多还原

【Abstract】 Due to the difficulty of situation element extraction in network security situation awareness,a mechanism for network security situation extraction based on Particle Swarm Optimization(PSO) is proposed.To improve the study ability of neural network,the method uses fuzzy logic technology to pre-fuzz the input historical situation element and then transforms them into fuzzy logic rule mapped between neural network layers.Meanwhile,PSO is used to optimize the connection weight of neural network in order to improve the study accuracy and velocity of neural network.Experiment results show that this model is an effective extraction technology of situation element.更多还原