基于IPSO-BiLSTM的网络安全态势预测  

Network Security Situation Prediction Based on IPSO-BiLSTM

作　　者：赵冬梅[1,2] 吴亚星 张红斌[3] ZHAO Dong-mei;WU Ya-xing;ZHANG Hong-bin(College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024,China;Hebei Key Laboratory of Network and Information Security,Hebei Normal University,Shijiazhuang 050024,China;School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang 050018,China)

机构地区：[1]河北师范大学计算机与网络空间安全学院,石家庄050024 [2]河北师范大学河北省网络与信息安全重点实验室,石家庄050024 [3]河北科技大学信息科学与工程学院,石家庄050018

出　　处：《计算机科学》2022年第7期357-362,共6页Computer Science

基　　金：国家自然科学基金(61672206);中央引导地方科技发展资金项目(216Z0701G);河北省重点研发计划(20310701D);河北省自然科学基金(F2019205163)。

摘　　要：针对复杂的网络安全态势预测问题,为了提高预测的收敛速度和预测精度,提出了一种基于改进粒子群优化双向长短期记忆(IPSO-BiLSTM)网络的网络安全态势预测模型。首先,针对所用数据集没有真实态势值的问题,采用了一种基于攻击影响的态势值计算方法,用于态势预测。其次,针对粒子群(PSO)算法易陷入局部最优值、搜索能力不均衡等问题,对惯性权重和加速因子进行改进,改进后的粒子群(IPSO)算法的全局和局部搜索能力平衡,收敛速度更快。最后,使用IPSO优化双向长短期记忆(BiLSTM)网络参数,提升预测能力。实验结果表明,IPSO-BiLSTM的拟合程度可达0.9946,其拟合效果和收敛速度均优于其他模型。Aiming at the complex network security situation prediction problem,a network security situation prediction model based on improved particle swarm optimization bidirectional long-short term memory(IPSO-BILSTM)network is proposed to improve the convergence speed and prediction accuracy.Firstly,in view of the lack of real situation value in the data set,a situation value calculation method based on attack influence is adopted for situation prediction.Secondly,to address the problems that particle swarm optimization(PSO)algorithm is prone to fall into local optima and unbalanced search capability,the inertia weights and acceleration factors are improved,and the improved particle swarm optimization(IPSO)algorithm has balanced global and local search capability and faster convergence speed.Finally,IPSO is used to optimize the parameters of bidirectional long short term memory(BiLSTM)network,so as to improve the prediction ability.Experimental results show that the fitting degree of IPSO-BiLSTM can reach 0.9946,and the fitting effect and convergence speed are better than other models.

关 键 词：网络安全 态势预测 神经网络 双向长短期记忆网络 改进粒子群优化 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]