基于Bi-IndRNN的恶意URL分析与检测

王欢欢; 田生伟; 禹龙; 彭咏芳; 裴新军

doi:10.13568/j.cnki.651094.2019.02.008

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基于Bi-IndRNN的恶意URL分析与检测

信息科学与技术 | 更新时间：2026-01-21

- 基于Bi-IndRNN的恶意URL分析与检测
- 基于Bi-IndRNN的恶意URL分析与检测
- 新疆大学学报（自然科学版中英文） 2019年36卷第2期页码：174-181
- 作者机构：
  
  1. 新疆大学软件学院
  2. 新疆大学网络中心
  3. 新疆大学信息科学与工程学院
- 作者简介：
- 基金信息：
  
  新疆维吾尔自治区科技人才培养项目(QN2016YX0051);新疆维吾尔自治区研究生科研创新项目(XJGRI2017007);赛尔网络下一代互联网技术创新项目(NGII20170420);天山青年计划(2017Q011)
- DOI：10.13568/j.cnki.651094.2019.02.008
  中图分类号： TP393.08
- 纸质出版：2019
- 稿件说明：
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[1]王欢欢,田生伟,禹龙,等.基于Bi-IndRNN的恶意URL分析与检测[J],2019,36(02):174-181.
[1]王欢欢,田生伟,禹龙,等.基于Bi-IndRNN的恶意URL分析与检测[J],2019,36(02):174-181. DOI： 10.13568/j.cnki.651094.2019.02.008.

DOI：10.13568/j.cnki.651094.2019.02.008.

摘要

本文提出一种基于双向IndRNN(Bidirectional Independently Recurrent Neural Network

Bi-IndRNN)的恶意URL分析与检测算法.通过对恶意URL分析与检测特点的研究

提取主机信息特征和URL信息特征.把主机信息特征与URL信息特征相融合

并利用Bi-IndRNN算法对恶意URL进行分析与检测.与k最邻近分类算法(k-NearestNeighbor

KNN)、高斯贝叶斯算法(GaussionNB)、LSTM(Long Short-Term Memory)算法、IndRNN(Independently Recurrent Neural Network)算法对比结果表明

该模型对恶意URL的分类检测准确率达到95.92%

明显高于其它算法模型.

Abstract

This paper proposes a malicious URL analysis and detection algorithm based on bidirectional IndRNN(Bidirectional Independently Recurrent Neural Network

Bi-IndRNN).Through the research on the analysis and detection characteristics of malicious URLs

the characteristics of host information and URL information are extracted.The host information feature is merged with the URL information feature

and the malicious URL is analyzed and detected by Bi-IndRNN algorithm.And comparing k nearest neighbor classification algorithm(k-NearestNeighbor

KNN)and Gaussian Bay algorithm(GaussionNB)

LSTM(Long Short-Term Memory)algorithm

IndRNN(Independently Recurrent Neural Network)algorithm.The experimental results show that the detection accuracy of the model for malicious URLs is 95.92%

which is significantly higher than other algorithm models.

关键词

Keywords

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