Botnet traffic detection based on random forest algorithm

XIAO Qi; SU Kai-yu

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Botnet traffic detection based on random forest algorithm

更新时间：2026-09-04

- Botnet traffic detection based on random forest algorithm
- Vol. 36, Issue 3, Pages: 43-47(2019)
- 作者机构：
  
  1. 中国计量大学机构信息工程学院,浙江,杭州,310018
  2. 中国计量大学现代教育技术中心,浙江,杭州,310018
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
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肖琦, 苏开宇. 基于随机森林的僵尸网络流量检测[J]. 微电子学与计算机, 2019, 36(3): 43-47.

XIAO Qi, SU Kai-yu. Botnet traffic detection based on random forest algorithm[J]. 2019, 36(3): 43-47.
肖琦, 苏开宇. 基于随机森林的僵尸网络流量检测[J]. 微电子学与计算机, 2019, 36(3): 43-47. DOI：

XIAO Qi, SU Kai-yu. Botnet traffic detection based on random forest algorithm[J]. 2019, 36(3): 43-47. DOI：

摘要

近年来

僵尸网络已经成为互联网领域所面临的严重的安全威胁之一.僵尸网络的控制者使用僵尸程序进行例如DDOS攻击

发送垃圾邮件

盗取敏感信息等一系列恶意活动.在海量的互联网流量中

从正常流量中有效的识别出僵尸网络流量是亟待解决的问题.本文的研究目的是分析和识别僵尸网络流量.实验在真实的包含僵尸网络流量数据中提取了9种特征

采用随机森林算法作为训练模型

与5种其他机器学习算法进行比较

实验结果表明本文提出的模型在给出的评价标准下性能最优.

Abstract

In recent years

Botnets have become one of the serious security threats in the Internet field. Botnet controllers used bots process to perform a series of malicious activities

such as DDOS attacks

sending spam

stealing sensitive user information

and so on. Identification of botnet traffic from normal traffic in the massive internet traffic is an urgent problem. The purpose of this paper was to analyze and identify botnet traffic. The experiment extracted 9 features from the real botnet traffic data

used the random forest algorithm as the training model and compared it with 5 other machine learning algorithms. The experimental results show that the proposed model has the best performance under the given evaluation criteria.

关键词

Keywords

references

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