基于反向传播算法的网络安全态势感知仿真
Simulation of Network Security Situation Awareness Based on Back Propagation Algorithm
张婷婷 1王智强2
作者信息
- 1. 山西警察学院网络安全保卫系,山西 太原 030400
- 2. 山西大学计算机与信息技术学院,山西 太原 030400
- 折叠
摘要
随着互联网技术的广泛应用,网络信息传输的数量日益提升,网络安全态势感知的需求也逐渐增加.针对当前网络安全态势感知算法检测准确率率低,误差较大等问题,提出了基于反向传播算法的网络安全态势感知模型.首先采用大数据分析方法对入侵信息的特征按节点分解并进行分段分析;其次通过切换检测信道和空间节点的分布式融合方法对关键节点进行分析,提取入侵数据的特征;然后通过反向传播算法对基本的感知原理进行优化,以减小模型检测过程中的误差;最后基于信息融合的结果进行优化,通过模糊识别的方法对入侵行为进行检测,达到安全态势感知的效果.实验结果表明,相比其它算法,所提模型将平均绝对误差缩小近5%,预测精确度提升至少7%,有最佳的实验效果,推动了网络安全态势感知技术的发展和应用.
Abstract
With the wide application of Internet technology,the number of network information transmission is in-creasing,and the demand for network security situational awareness is also increasing.Aiming at the problems of low detection accuracy and large error of current network security situation awareness algorithm,this paper proposes a net-work security situation awareness model based on the back propagation algorithm.First,the big data analysis method was used to decompose the features of intrusion information by node and analyze them by segment;Secondly,the key nodes were analyzed by the distributed fusion method of switching detection channels and spatial nodes,and the fea-tures of intrusion data were extracted;Then,the basic perception principle was optimized through the back propagation algorithm to reduce the error in the process of model detection;Finally,based on the results of information fusion,the intrusion behavior was detected by fuzzy recognition method to achieve the effect of security situation awareness.The experimental results show that compared with other algorithms,the proposed model reduces the average absolute error by nearly 5%,and improves the prediction accuracy by at least 7%.It has the best experimental effect,promoting the development and application of network security situational awareness technology.
关键词
网络安全态势感知/反向传播算法/入侵检测/无线传感节点Key words
Network security situational awareness/Back propagation algorithm/Intrusion detection/wireless sensor node引用本文复制引用
基金项目
山西省哲学社会科学规划项目(2022)(2022YD164)
出版年
2024
NETL
NSTL
万方数据