Network Intrusion Detection for Web Security Scanner

Liu Zile; Wu Xiaoling; Feng Yongjin; Hoon Heo

doi:10.12052/gdutxb.240151

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Network Intrusion Detection for Web Security Scanner

更新时间：2026-05-27

- Network Intrusion Detection for Web Security Scanner
- Vol. 42, Issue 6, Pages: 52-61(2025)
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 三星电机有限公司,京畿道,水原市,韩国,16674
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240151
  CLC： TP391
- Published：2025
- 稿件说明：
移动端阅览
刘梓乐, 吴晓鸰, 冯永晋, 等. 面向Web安全扫描器的网络入侵检测研究[J]. 广东工业大学学报, 2025, 42(6): 52–61.

Liu Zile, Wu Xiaoling, Feng Yongjin, et al. Network Intrusion Detection for Web Security Scanner[J]. 2025, 42(6): 52-61.
刘梓乐, 吴晓鸰, 冯永晋, 等. 面向Web安全扫描器的网络入侵检测研究[J]. 广东工业大学学报, 2025, 42(6): 52–61. DOI： 10.12052/gdutxb.240151

Liu Zile, Wu Xiaoling, Feng Yongjin, et al. Network Intrusion Detection for Web Security Scanner[J]. 2025, 42(6): 52-61. DOI： 10.12052/gdutxb.240151.

摘要

在网络攻击中，使用Web安全扫描器的扫描探测是Web攻击前期侦察的主要手段。现有的Web入侵检测方法侧重于对已知攻击数据和正常数据进行分类，存在容易被绕过、无法检测未知攻击等问题。为了弥补现有的缺陷，本文提出从Web安全扫描器探测扫描的角度检测Web攻击。首先，通过实验采集了多种Web安全扫描器的扫描数据，并分析、验证了扫描数据间存在的相似性。然后，提出了一种基于扫描数据的Web入侵检测方案(CharEmbedding-CGRU(

) )，其中包括特征提取方法及卷积门控循环网络模型。最后，在收集的扫描数据集上进行对比实验，结果表明，本文提出的Web入侵检测方案具有更好的攻击检测效果，对已知Web安全扫描器的检测精确率是99.87%、

值为98.99%，对未知Web安全扫描器的检测精确率为92.98%、

值为95.71%。

Abstract

The use of Web security scanners for scanning and probing serves as a primary way of cyber reconnaissance in the early stages of Web attacks. Existing Web intrusion detection methods primarily focus on addressing the binary classification problem between known attack flows and normal flows

which have issues such as being easily bypassed and an inability to detect unknown attacks. To address these

this paper proposes a method for detecting Web intrusion attacks from the perspective of Web security scanner scanning. Firstly

this paper collects scanning data from various Web security s

canners through experiments

and analyzes the similarity of the scanning data. Secondly

a Web intrusion detection scheme based on scanning data is proposed

which includes a feature extraction method and a convolutional gated recurrent network model. Finally

comparative experiments are conducted on the collected dataset

and the results show that the Web intrusion detection scheme proposed in this paper achieves superior attack detection performance. Specifically

it achieves a detection precision of 99.87% and an

-score of 98.99% for known Web security scanners

and a detection precision of 92.98% and an

-score of 95.71% for unknown Web security scanners.

关键词

Keywords

references

DENG M L, KAN Y P, SUN C C, et al . Summary of network intrusion detection systems based on deep learning[J ] . Journal of Computer Applications, 2025, 45(2): 453-466.

SEPCZUK M. Dynamic Web application firewall detection supported by cyber mimic defense approach[J]. Journal of Network and Computer Applications, 2023, 213: 103596.

TEKEREK A. A novel architecture for Web-based attack detection using convolutional neural network[J]. Computers Security, 2021, 100: 102096.

ZUECH R, HANCOCK J, KHOSHGOFTAAR T M. Detecting Web attacks using random undersampling and ensemble learners[J]. Journal of Big Data, 2021, 8(1): 1-20.

TOPRAK S, YAVUZ A G. Web application firewall based on anomaly detection using deep learning[J]. Acta Infologica, 2022, 6(2): 219-244.

FAN Y C, LIU X K, ZHU J S, et al . Research on Web attack detection of service website based on BERT[J ] . Computer Technology and Development, 2022, 32(8): 168-173.

ZHOU Q, ZHAI J T, JIA D S, et al . A Web attack detection method based on convolutional gated recurrent neural network[J ] . Journal of Guangxi Normal University (Natural Science Edition) , 2023, 41(6): 51-61.

DAWADI B R, ADHIKARI B, SRIVASTAVA D K. Deep learning technique-enabled Web application firewall for the detection of Web attacks[J]. Sensors, 2023, 23(4): 2073.

SUN H Z, WANG J, WANG P, et al . Network intrusion detection method based on Attention-BiTCN[J ] . Netinfo Security, 2024, 24(2): 309-318.

ZHANG Z Y, CHEN Y W, WANG Y H. Intrusion detection architecture for ICS based on XGBoost-DNN[J]. Journal of Harbin Engineering University, 2024, 45(11): 2243-2249.

CHEN H, CHENG M J, JIN H B, et al . Intrusion detection model i ncorporating contrastive learning and feature selection[J ] . Journal of Information Security Research, 2024, 10(5): 453-461.

GONG X Y, LAI Y, LI N, et al . Network intrusion detection model based on multiscale fusion and spatial-temporal feature[J ] . Computer Engineering and Design, 2024, 45(6): 1640-1646.

LIU T, MENG X Q. Feature selection of intrusion detection based on improved sparrow search algorithm[J]. Computer Engineering and Design, 2024, 45(4): 989-996.

BAO H F, LI W H, WANG X Y, et al . Payload level graph attention network for web attack traffic detection[C ] //International Conference on Computational Science. Cham: Springer Nature Switzerland, 2023: 394-407.

SHEN X L, LIU S F. Intrusion detection model based on graph edge feature attention[J]. Computer Engineering, 2024, 50(11): 236-245.

MAC H, TRUONG D, NGUYEN L, et al . Detecting attacks on Web applications using autoencoder[C ] //Proceedings of the 9th International Symposium on Information and Communication Technology. New York: ACM, 2018: 416-421.

SEVRI M, KARACAN H. Two stage deep learning based stacked ensemble model for Web application security[J]. KSII Transactions on Internet and Information Systems (TIIS) , 2022, 16(2): 632-657.

BENOVA L, HUDEC L. Comprehensive analysis and evaluation of anomalous user activity in Web server logs[J]. Sensors, 2024, 24(3): 746.

YIN Z L, HUANG Y X, YU Z T. AE-EM: a Web intrusion detection algorithm based on expectation maximization[J]. Computer Engineering and Applications, 2025, 61(3): 315-325.

JEMAL I, HADDAR M A, CHEIKHROUHOU O, et al . Performance evaluation of convolutional neural network for Web security[J ] . Computer Communications, 2021, 175: 58-67.

CHEN H, CHEN J, CHEN J, et al . An automatic vulnerability scanner for Web applications[C ] //2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and CommuNications (TrustCom) . Guangzhou: IEEE, 2020: 1519-1524.

段鹏飞. 基于Web应用安全扫描器的设计与实现[D]. 兰州: 西北师范大学, 2021.

郝子希. 基于渗透技术的Web应用漏洞扫描器设计与实现[D]. 上海: 东华大学, 2018.

LONG H Q, ZHOU H J, YAN Z J. Design and implementation of Web vulnerability scanning system[J]. Modern Information Technology, 2024, 8(12): 32-35.

GUO Z M, ZHOU J Y, WANG D, et al . Network intrusion detection method based on Transformer neural network model[J ] . Journal of Chongqing University, 2021, 44(11): 81-88.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Sentiment Classification Based on Double Attention Convolutional Neural Network Model

Ground Deformation Monitoring and Influencing Factors Analysis in Fuzhou City Based on PSI Technology

The Cross-twin Physics-informed Neural Network for Wave Equations with Conserved Quantities

Robust Design for Artificial Noise-assisted UAV Terahertz Secure Communication

Bimodal Iterative Cross-attention Fusion Ensemble Framework

Related Author

Zeng Bi-qing

Han Xu-li

Wang Sheng-yu

Xu Ru-yang

Zhou Wu

Related Institution

School of Software, South China Normal University

School of Computer Science, South China Normal University

AI问答

⁰