基于对抗性机器学习的大数据网络安全防御方法  

Big Data Network Security Defense Method Based on Adversarial Machine Learning

作　　者：张得震 ZHANG Dezhen(Baiyin Vocational College of Mining and Metallurgy,Baiyin Gansu 730900)

机构地区：[1]白银矿冶职业技术学院,甘肃白银730900

出　　处：《软件》2024年第9期148-150,共3页Software

摘　　要：由于大数据网络安全攻击数据的特征具有差异性,因此,网络安全防御对攻击数据的识别效果难以保障。本文提出基于对抗性机器学习的大数据网络安全防御方法。试验结果表明,本文算法对于不同类型攻击数据的漏检率始终处于较低水平,表现出了较高的稳定性。Due to the differences in the characteristics of big data network security attack data,it is difficult to guarantee the recognition effect of network security defense on attack data.This article proposes a big data network security defense method based on adversarial machine learning.The experimental results show that the algorithm proposed in this paper consistently maintains a low level of missed detection rate for different types of attack data,demonstrating high stability.

关 键 词：对抗性机器学习 大数据网络 安全防御 对抗样本 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]