The HoneyTank: A scalable approach to collect malicious internet traffic

被引：4
|
作者：
Vanderavero, Nicolas [1 ]
Brouckaert, Xavier [1 ]
Bonaventure, Olivier [1 ]
Le Charlier, Baudouin [1 ,2 ]
机构：
[1] Department of Computing Science and Engineering, Universiteá Catholique de Louvain (UCL), Belgium
[2] Computing Science Department, Catholic University of Louvain-la-Neuve
来源：
International Journal of Critical Infrastructures | 2008年 / 4卷 / 1-2期
关键词：
Computational methods - Computer worms - Internet protocols - Intrusion detection;
D O I：
10.1504/IJCIS.2008.016100
中图分类号：
学科分类号：
摘要：
In this paper, we propose an efficient method for collecting large amounts of malicious internet traffic. The key advantage of our method is that it does not need to maintain any state to emulate TCP services running on a large number of emulated end-systems. We implemented a prototype on the ASAX intrusion detection system and we provide several examples of the malicious activities that were collected on a campus network attached to the internet. We explain how we implemented various protocols in a stateless way. We also discuss how our method can be improved to make an accurate but still stateless emulation of stateful protocols. Copyright © 2008 Inderscience Enterprises Ltd.
引用
收藏
页码：185 / 205
相关论文
共 50 条
  • [31] QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams
    Zequn NIU
    Jingfeng XUE
    Yong WANG
    Tianwei LEI
    Weijie HAN
    Xianwei GAO
    Chinese Journal of Electronics, 2024, 33 (03) : 645 - 656
  • [32] Universal Network Traffic Analysis for Malicious Traffic Detection using RappNet: A Privacy-Preserving Approach
    Barut, Onur
    Deng, Lulin
    Lu, David
    2023 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY, CNS, 2023,
  • [33] QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams
    Niu, Zequn
    Xue, Jingfeng
    Wang, Yong
    Lei, Tianwei
    Han, Weijie
    Gao, Xianwei
    CHINESE JOURNAL OF ELECTRONICS, 2024, 33 (03) : 645 - 656
  • [34] ON THE USE OF THE INTERNET TO COLLECT EARTHQUAKE INFORMATION
    Bossu, Remy
    Earle, Paul S.
    ANNALS OF GEOPHYSICS, 2011, 54 (06) : 672 - 672
  • [35] A lightweight model design approach for few-shot malicious traffic classification
    Wang, Ruonan
    Huang, Minhuan
    Zhao, Jinjing
    Zhang, Hongzheng
    Zhong, Wenjing
    Zhang, Zhaowei
    He, Liqiang
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [36] SFMD: A Semi-Supervised Federated Malicious Traffic Detection Approach in IoT
    Wang, Wenyue
    Wang, Shanshan
    Bai, Daokuan
    Zhao, Chuan
    Peng, Lizhi
    Chen, Zhenxiang
    2022 IEEE INTL CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, BIG DATA & CLOUD COMPUTING, SUSTAINABLE COMPUTING & COMMUNICATIONS, SOCIAL COMPUTING & NETWORKING, ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM, 2022, : 774 - 781
  • [37] An Entropy and Volume-based Approach for Identifying Malicious Activities in Honeynet Traffic
    Sqalli, Mohammed H.
    Firdous, Syed Naeem
    Baig, Zubair
    Azzedin, Farag
    2011 INTERNATIONAL CONFERENCE ON CYBERWORLDS, 2011, : 23 - 30
  • [38] A blockchain and stacked machine learning approach for malicious nodes’ detection in internet of things
    Shakira Musa Baig
    Muhammad Umar Javed
    Ahmad Almogren
    Nadeem Javaid
    Mohsin Jamil
    Peer-to-Peer Networking and Applications, 2023, 16 : 2811 - 2832
  • [39] A blockchain and stacked machine learning approach for malicious nodes' detection in internet of things
    Baig, Shakira Musa
    Javed, Muhammad Umar
    Almogren, Ahmad
    Javaid, Nadeem
    Jamil, Mohsin
    PEER-TO-PEER NETWORKING AND APPLICATIONS, 2023, 16 (06) : 2811 - 2832
  • [40] Research on malicious traffic identification technology in encrypted traffic
    Zeng Y.
    Wu Z.
    Dong L.
    Liu Z.
    Ma J.
    Li Z.
    Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2021, 48 (03): : 170 - 187
12345