信息安全研究2024，Vol.10Issue(1)：34-39,6.DOI:10.12379/j.issn.2096-1057.2024.01.06
基于自适应集成学习的异常流量检测
Abnormal Traffic Detection Based on Adaptive Integrated Learning
摘要
Abstract
We propose an adaptive integrate-learning-based anomalous traffic detection method in this paper that uses the discrete Fourier transform to extract the frequency domain features of traffic,resulting in less information loss during the extraction of traffic features.An evaluation metric based on stability and accuracy fluctuations is used to dynamically assess the reliability of the current traffic features,and the feature data blocks that pass the evaluation are used to generate new sub-classifiers.Meanwhile,an integrated adaptive classifier is designed,whose parameters and sub-classifiers are adjusted in real time according to the current situation.The experimental results show that the method is effective for solving the concept drift problem in anomalous traffic detection and machine learning against attacks.关键词
异常流量检测/频域特征/概念漂移/集成学习/自适应学习Key words
anomalous traffic detection/frequency domain feature/concept drift/integration learning/adaptive learning分类
信息技术与安全科学引用本文复制引用
倪嘉翼,陈伟,童家铖,李频..基于自适应集成学习的异常流量检测[J].信息安全研究,2024,10(1):34-39,6.基金项目
国家重点研发计划项目(2019YFB2101704) （2019YFB2101704）
