基于优化蜂群算法的隐蔽性网络攻击行为自适应辨识模型  

Adaptive identification model of covert network attack behavior based on optimized bee colony algorithm

作　　者：李伯恺 LI Bokai(Beijing Xianglong Assets Management Co.,Ltd.,Beijing 100053,China)

机构地区：[1]北京祥龙资产经营有限责任公司,北京100053

出　　处：《电子设计工程》2024年第16期97-101,共5页Electronic Design Engineering

摘　　要：为了提高隐蔽性网络攻击行为的自适应辨识性能,有效地防范隐蔽性网络攻击威胁,保护网络系统的安全,提出一种基于优化蜂群算法的隐蔽性网络攻击行为自适应辨识模型。通过分析网络节点接收信号的能量变化,引入优化蜂群算法,定位隐蔽性网络攻击节点;利用小波变换计算网络攻击行为数据的信息熵,提取隐蔽性网络攻击行为特征;通过网络攻击行为的干扰抑制,分析隐蔽性网络遭到攻击的振荡衰减情况,判断攻击行为数据的噪声是否属于高斯噪声,实现隐蔽性网络攻击行为的自适应辨识。实验结果表明,文中模型能够辨识隐蔽性网络的攻击行为,对网络攻击行为的误识率均在5%以内,可以有效确定隐蔽性网络攻击节点的位置,降低网络攻击行为的误识率,提高网络安全性能。In order to improve the adaptive identification performance of covert network attacks,effectively prevent the threat of covert network attacks,and protect the security of network systems,an adaptive identification model of covert network attack behavior based on optimized bee colony algorithm was proposed.By analyzing the energy changes of network nodes’received signals,an optimized bee colony algorithm was introduced to locate convert network attack nodes.The information entropy of network attack behavior data was calculated by wavelet transform,and the characteristics of covert network attack behavior were extracted.Through interference suppression of network attack behavior,the oscillation attenuation of covert network under attack was analyzed,and it was determined whether the noise of attack behavior data belonged to Gaussian noise,thus achieving adaptive identification of covert network attack behavior.The experimental results showed that the model could identify the attack behavior of covert network,and the false recognition rate of network attack behavior was within 5%.It can effectively determine the location of convert network attack nodes,reduce the false recognition rate of network attack behavior,and improve network security performance.

关 键 词：优化蜂群算法 攻击行为 特征提取 辨识模型 隐蔽性网络 自适应 

分 类 号：TN97[电子电信—信号与信息处理]