基于增量式学习的复杂网络节点攻击检测算法  

Node attack detection algorithm for complex networks based on incremental learning

作　　者：杨志飞[1] 张佳 李泽阳 YANG Zhi-fei;ZHANG Jia;LIZe-yang(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070

出　　处：《吉林大学学报（工学版）》2024年第10期2963-2968,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：教育部人文社会科学研究项目(20YJCZH212);甘肃省教育厅高等学校科研项目(2020B-115);兰州交通大学研究生创新之星项目(2021CXZX-558);国家自然科学基金项目(62062049,61962034)。

摘　　要：为了避免网络节点攻击产生的弊端,本文提出了基于增量式学习的复杂网络节点攻击检测算法。该方法首先利用阈值自学习的方式对待检测复杂网络中所含信号实施去噪处理;其次利用支持向量机与网络节点攻击特征提取原则结合的方式实现复杂网络节点攻击特征的精准提取;最后将提取的节点攻击特征输入改进的混合神经网络中并展开增量式学习,以此实现节点攻击特征的分类,实现复杂网络节点攻击精准检测的目的。经实验验证,利用本文方法能够平稳高效地对各类复杂网络中的节点攻击进行精准检测。In order to avoid the drawbacks caused by network node attacks,a complex network node attack detection algorithm based on incremental learning is proposed.Firstly,this method utilizes threshold selflearning to denoise the signals contained in the detected complex network;Secondiy,uses a combination of support vector machine and network node attack feature extraction principles to achieve accurate extraction of complex network node attack features;Finally,the extracted node attack features are input into an improved hybrid neural network and incremental learning is carried out to achieve the classification of node attack features and achieve precise detection of complex network node attacks.Through experimental verification,the proposed method can accurately detect node attacks in various complex networks smoothly and efficiently.

关 键 词：增量式学习 复杂网络 攻击检测算法 网络去噪 特征提取 

分 类 号：TM764.22[电气工程—电力系统及自动化]