Network security situation prediction based on improved particle swarm optimization and extreme learning machine

doi:10.11772/j.issn.1001-9081.2020060924

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (3): 768-773.DOI: 10.11772/j.issn.1001-9081.2020060924

Special Issue: 网络空间安全

• Cyber security • Previous Articles Next Articles

Network security situation prediction based on improved particle swarm optimization and extreme learning machine

TANG Yanqiang^1,2, LI Chenghai², SONG Yafei²

1. Graduate School, Air Force Engineering University, Xi'an Shaanxi 710051, China;
2. College of Air Defense and Missile Defense, Air Force Engineering University, Xi'an Shaanxi 710051, China

Received:2020-06-30 Revised:2020-10-05 Online:2021-03-10 Published:2021-01-15
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61703426).

基于改进粒子群优化和极限学习机的网络安全态势预测

唐延强^1,2, 李成海², 宋亚飞²

1. 空军工程大学研究生院, 西安 710051;
2. 空军工程大学防空反导学院, 西安 710051

通讯作者: 李成海
作者简介:唐延强(1997-),男,山东莱西人,硕士研究生,主要研究方向:入侵检测、网络安全态势感知;李成海(1966-),男,山东东平人,教授,博士,主要研究方向:网络安全态势感知、嵌入式操作系统;宋亚飞(1988-),男,河南汝州人,讲师,博士,CCF会员,主要研究方向:智能推理与决策、目标意图识别。
基金资助:
国家自然科学基金资助项目（61703426）。

Abstract

Abstract: Focusing on the problems of low prediction accuracy and slow convergence speed of network security situation prediction model, a prediction method based on Improved Particle Swarm Optimization Extreme Learning Machine (IPSO-ELM) algorithm was proposed. Firstly, the inertia weight and learning factor of Particle Swarm Optimization (PSO) algorithm were improved to realize the adaptive adjustment of the two parameters with the increase of iteration times, so that PSO had a large search range and fast speed at the initial stage, strong convergence ability and stability at the later stage. Secondly, aiming at the problem that PSO is easy to fall into the local optimum, a particle stagnation disturbance strategy was proposed to re-guide the particles trapped in the local optimum to the global optimal flying. The Improved Particle Swarm Optimization (IPSO) algorithm obtained in this way ensured the global optimization ability and enhanced the local search ability. Finally, IPSO was combined with Extreme Learning Machine (ELM) to optimize the initial weights and thresholds of ELM. Compared with ELM, the ELM combining with IPSO had the prediction accuracy improved by 44.25%. Experimental results show that, compared with PSO-ELM, IPSO-ELM has the fitting degree of prediction results reached 0.99, and the convergence rate increased by 47.43%. The proposed algorithm is obviously better than the comparison algorithms in the prediction accuracy and convergence speed.

Key words: network security, situation prediction, Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM), neural network, inertia weight

摘要： 针对网络安全态势预测模型预测精度不高、收敛较慢等问题，提出了一种基于改进粒子群优化极限学习机（IPSO-ELM）算法的预测方法。首先，通过改进粒子群优化（PSO）算法中的惯性权重和学习因子来实现两种参数随着迭代次数增加的自适应调整，使PSO初期搜索范围大、速度高，后期收敛能力强、稳定。其次，针对PSO易陷入局部最优的问题，提出一种粒子停滞扰动策略，将陷入局部最优的粒子重新引导至全局最优飞行。改进粒子群优化（IPSO）算法既保证了全局寻优的能力，又对局部搜索能力有所增强。最后，将IPSO与极限学习机（ELM）结合来优化ELM的初始权值及阈值。与ELM相比，结合IPSO的ELM的预测精度提高了44.25%。实验结果表明，与PSO-ELM相比，IPSO-ELM的预测结果拟合度可达到0.99，收敛速度提升了47.43%。所提算法在预测精度和收敛速度等指标上明显优于对比算法。

关键词: 网络安全, 态势预测, 粒子群优化, 极限学习机, 神经网络, 惯性权重

CLC Number:

TP393.08

TANG Yanqiang, LI Chenghai, SONG Yafei. Network security situation prediction based on improved particle swarm optimization and extreme learning machine[J]. Journal of Computer Applications, 2021, 41(3): 768-773.

唐延强, 李成海, 宋亚飞. 基于改进粒子群优化和极限学习机的网络安全态势预测[J]. 计算机应用, 2021, 41(3): 768-773.

References

[1] 张志勇, 张文博, 杨慧, 等. 网络安全态势预测研究综述[J]. 通信技术,2019,52(7):1713-1721.(ZHANG Z Y,ZHANG W B, YANG H,et al. A survey of research on network threat prediction[J]. Communications Technology,2019,52(7):1713-1721.)
[2] 余建, 林志兴, 谢彬. 灰色关联模型的网络安全态势感识预测方法[J]. 实验室研究与探索,2019,38(2):31-35, 70.(YU J, LIN Z X,XIE B. Network security situation awareness prediction method based on grey correlation model[J]. Research and Exploration in Laboratory,2019,38(2):31-35,70.)
[3] 张然, 刘敏, 张启坤, 等. 基于SOA_BP神经网络的网络安全态势预测算法研究[J]. 微电子学与计算机,2020,37(6):62-65, 69.(ZHANG R,LIU M,ZHANG Q K,et al. Research on network security situation prediction algorithm based on SOA_BP neural network[J]. Microelectronics and Computer,2020,37(6):62-65,69.)
[4] 刘俊男,陈占芳,姜晓明,等. 用于网络安全态势预测的SAGPSO-SVM模型研究[J]. 长春理工大学学报(自然科学版), 2019,42(6):126-128.(LIU J N,CHEN Z F,JIANG X M,et al. Research on SAGPSO-SVM model for network security situation prediction[J]. Journal of Changchun University of Science and Technology(Natural Science Edition),2019,42(6):126-128.)
[5] HU G,ZHOU Z,HU C,et al. Hidden behavior prediction of complex system based on time-delay belief rule base forecasting model[J]. Knowledge-Based Systems,2020,203:No. 106147.
[6] HUANG G B,ZHU Q Y,SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing,2005,70(1/2/3):489-501.
[7] FENG H,YIN C,WENG W,et al. Robotic excavator trajectory control using an improved GA based PID controller[J]. Mechanical Systems and Signal Processing,2018,105:153-168.
[8] LI X,WU D,HE J,et al. An improved method of particle swarm optimization for path planning of mobile robot[J]. Journal of Control Science and Engineering,2020,2020:No. 3857894.
[9] 孙卫喜. 用于网络安全态势预测的粒子群与支持向量机算法研究[J]. 计算机应用与软件,2019,36(6):308-316.(SUN W X. PSO and SVM for network security situation prediction[J]. Computer Applications and Software,2019,36(6):308-316.)
[10] WANG X,WANG C,LI Q. Short-term wind power prediction using GA-ELM[J]. The Open Electrical and Electronic Engineering Journal,2017,11:48-56.
[11] 蒋建东, 余沣, 董存, 等. 基于PSO与ELM组合算法的短期光伏发电功率预测模型[J]. 郑州大学学报(理学版),2019,51(3):120-126.(JIANG J D,YU F,DONG C,et al. A short-term photovoltaic power forecasting model based on PSO and ELM combined algorithm[J]. Journal of Zhengzhou University(Natural Science Edition),2019,51(3):120-126.)
[12] KENNEDY J,EBERHART R. Particle swarm optimization[C]//Proceedings of the 1995 International Conference on Neural Networks. Piscataway:IEEE,1995:1942-1948.
[13] 魏勇, 赵开新, 王东署. 改进粒子群算法在移动机器人路径规划中的应用[J]. 火力与指挥控制,2018,43(2):41-43.(WEI Y,ZHAO K X,WANG D S. Application of improved particle swarm optimization algorithm in path planning of mobile robot[J]. Fire Control and Command Control,2018,43(2):41-43.)
[14] 黄轩, 张军, 詹志辉. 基于随机惯量权重的快速粒子群优化算法[J]. 计算机工程与设计,2009,30(3):647-650,663. (HUANG X,ZHANG J,ZHAN Z H. Faster particle swarm optimization with random inertia weight[J]. Computer Engineering and Design,2009,30(3):647-650,663.)
[15] RATNAWEERA A,HALGAMUGE S K,WATSON H C. Selforganizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[J]. IEEE Transactions on Evolutionary Computation,2004,8(3):240-255.
[16] 江洋, 李成海, 魏晓辉, 等. 改进PSO优化RBF的网络安全态势预测研究[J]. 测控技术,2018,37(5):56-60.(JIANG Y,LI C H,WEI X H,et al. Research on network security situation prediction based on RBF optimized by improved PSO[J]. Measurement and Control Technology,2018,37(5):56-60.)

Network security situation prediction based on improved particle swarm optimization and extreme learning machine

基于改进粒子群优化和极限学习机的网络安全态势预测

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