arrow
返回

Intelligent Network Device Identification Based on Active TCP/IP Stack Probing

delete2024-11-01
delete1
PRE
AI
L
Libing Qiao
董
董恩焕 (Dong, Enhuan) *
H
Huanpu Yin
H
Haisheng Li *
杨家海 封面图
杨家海 (Jiahai Yang)
DOI:10.1109/MNET.2024.3374080delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
With the continuous development of network devices, there are increasingly types and quantities of network devices. Accurate identification of device types helps proactively protect potentially vulnerable devices exposed on the Internet. Among the network device identification methods, the TCP/IP stack active detection method is an important kind since it does not require many open ports of target devices. However, its performance is limited by the rule/fingerprint database quality. Maintaining such a database requires a lot of expert effort, making the method difficult to scale up. To solve the scalability problem, our insight in this paper is to use machine learning methods to generate network device classifiers without needing expert effort. However, generating labeled datasets, extracting features, and selecting features without expert effort is nontrivial. We propose IntelliNDI, an intelligent and active network device identification method. IntelliNDI collects network device type information from multiple cyberspace search engines and filter out the network devices with different types on different search engines. We regard the approximately consistent network device types as the ground truth network device types. As for feature extraction, we use the same attributes employed in the well-known Nmap protocol stack detection to avoid the requirements for constructing features. Finally, we select features with basic ML methods. We implement IntelliNDI for several kinds of typical network devices. The trained classifiers in our experiments can achieve 90 percent accuracy.
Keyword:
Feature extraction
Object recognition
TCPIP
Protocols
Databases
Monitoring
Machine learning
Network Device Identification
Active TCP/IP Stack Probing

期刊

IEEE Network 封面图
IEEE Network
IF:
6.3
论文数:
2.7K
被引数:
1.1W

机构

暂无机构信息
引用论文

引用论文

err分享
err收藏
Autonomes Fahren
err
IF0
err2015-01-01
err0
PREAI
err
err分享
err收藏
A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets
err2021-01-01
err63
errOAAI
errSanchez, Pedro Miguel Sanchez; Valero, Jose Maria Jorquera; Celdran, Alberto Huertas; Bovet, Gerome; Perez, Manuel Gil; Perez, Gregorio Martinez
err分享
err收藏
没有更多内容