基于对抗性机器学习的网络欺骗攻击模式辨识研究  

Research on network spoofing attack pattern identification based on adversarial machine learning

作　　者：杨鹏[1] 郭思莹 YANG Peng;GUO Siying(School of Computer Science and Engineering,North Minzu University,Yinchuan 750000,China)

机构地区：[1]北方民族大学计算机科学与工程学院,宁夏银川750000

出　　处：《现代电子技术》2025年第5期86-90,共5页Modern Electronics Technique

基　　金：宁夏自然科学基金资助项目(2023AAC03310);北方民族大学人才引进科研项目(2023QNPY06)。

摘　　要：为精准、自动辨识网络欺骗攻击模式,提升网络传输安全性,提出基于对抗性机器学习的网络欺骗攻击模式辨识方法。该方法提取可描述网络流量的行为模式、分布状况以及流量间相互关系的网络流表特征集,将其输入生成对抗网络中进行训练,构建网络欺骗攻击模式辨识模型;生成器在损失函数的指导下生成接近真实样本的数据集,再将其输入判别器中;判别器采用多层结构设计,将各个判别器的输出结果进行整合后获取其平均值作为最后的判断依据,结合权重矩阵对该结果进行投票,输出网络欺骗攻击模式辨识结果。测试结果显示,该方法能够可靠提取网络流表特征,各个网络欺骗攻击类别的平均绝对误差百分比结果均在0.014 0以下,最小结果仅为0.005 8,效果良好。A network spoofing attack pattern identification method based on adversarial machine learning(AML)is proposed to accurately and automatically identify the behavior patterns of network spoofing attacks and improve network transmission security.In this method,a feature set of network flow tables that can describe the behavior patterns,distribution status and interrelationships of network flow is extracted and input into a generative adversarial network(GAN)for training,so as to construct a network spoofing attack pattern identification model.Under the guidance of the loss function,the generator generates a dataset close to the real sample,and the dataset is then input into a discriminator.The discriminator is designed in a multi-layer structure.The output results of each discriminator are synthesized to obtain the average value,which is taken as the final judgment basis.In combination with the weight matrix,the result is subjected to vote to output the network spoofing attack recognition results.The test results show that the proposed method can extract network flow table features reliably.In addition,its mean absolute error(MAE)percentage for various types of network spoofing attacks is below 0.0140,with a minimum of only 0.0058,indicating good identification effect.

关 键 词：对抗性机器学习 网络欺骗 攻击模式辨识 生成器 判别器 网络流表特征 

分 类 号：TN711-34[电子电信—电路与系统] TM76[电气工程—电力系统及自动化]