IGAPSO-ELM:一种网络安全态势预测模型  

IGAPSO-ELM:A Model for Network Security Situation Prediction

作　　者：唐延强 李成海[2] 王坚[2] 王亚男[2] 曹波 TANG Yanqiang;LI Chenghai;WANG Jian;WANG Ya'nan;CAO Bo(Air Force Engineering University,Graduate School,Xi'an 710000 China;Air Force Engineering University,Air and Missile Defense College,Xi'an 710000 China)

机构地区：[1]空军工程大学研究生院,西安710000 [2]空军工程大学防空反导学院,西安710000

出　　处：《电光与控制》2022年第2期30-35,共6页Electronics Optics & Control

基　　金：国家自然科学基金(61703426);中国博士后科学基金(2018 M633680);陕西省高校科协青年人才托举计划(20190108)。

摘　　要：针对网络安全态势预测,为提高预测的精确度和预测算法的收敛速度,提出一种改进遗传粒子群算法优化极限学习机(IGAPSO-ELM)的预测方法。首先,改进GAPSO中的惯性权重和学习因子,通过定义动态指数函数使算法在执行的不同阶段实现两种参数自适应;其次,针对GAPSO中人为设定的固定交叉率和变异率,提出一种自适应交叉和变异策略;最后,以IGAPSO优化ELM的初始权值和偏差。IGAPSO既保证了种群的多样性,又提高了算法的收敛速度。通过仿真实验对比得出:IGAPSO-ELM对网络安全态势预测拟合度可达0.99,收敛速度相较于对比算法有大幅度提升。As for network security situation prediction,an Improved Genetic Algorithm and Particle Swarm Optimization(IGAPSO) is proposed to optimize Extreme Learning Machine(ELM) neural network,so as to obtain higher prediction precision and faster convergence rate.Firstly,the inertia weight and the learning factor in GAPSO are improved to realize self-adaptation at different stages of execution by defining the dynamic exponential function.Secondly,as for the fixed crossover rate and mutation rate in GAPSO,an adaptive crossover and mutation strategy is proposed.Finally,the IGAPSO is used to optimize the initial weights and deviations of ELM.IGAPSO not only ensures the diversity of the population,but also improves the convergence rate of the algorithm.The simulation results show that the fitting degree of IGAPSO-ELM for network security situation prediction can reach 0.99,and the convergence rate is greatly improved compared with that of the contrast algorithms.

关 键 词：网络安全态势预测 遗传粒子群算法 极限学习机 自适应调整 

分 类 号：TP393.8[自动化与计算机技术—计算机应用技术]