Flow Based Botnet Traffic Detection Using Machine Learning-Reference-Cited by-同舟云学术

Flow Based Botnet Traffic Detection Using Machine Learning

Published:2019-09-24 Issue: Volume: Page:418-426
ISSN:1876-1100
Container-title:Proceedings of ICETIT 2019
language:
Short-container-title:

Author:

Gahelot Parul,Dayal Neelam

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-30577-2_36

Reference19 articles.

1. ISOT Dataset (2010). https://www.uvic.ca/engineering/ece/isot/datasets/

2. Zeus Malware: Threat Banking Industry (2010). https://docplayer.net/23170032-Zeus-malware-threat-banking-industry.html

3. The CTU 13 Dataset (2011). https://www.stratosphereips.org/datasets-ctu13/

4. Canadian Institute for Cybersecurity. Botnet Dataset (2014). https://www.unb.ca/cic/datasets/botnet.html

5. Cisco Visual Networking Index: Forecast and Trends, 20172022 (2018). https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On Feature Selection Algorithms for Effective Botnet Detection;Journal of Network and Systems Management;2024-04

2. A Two-Phase Multi-Class Botnet Labeling Approach for Real-World Traffic;2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC);2024-02-19

3. Light weight frame work in network security for the health care to identify botnet in IoT by using stack ensemble deep learning;SSRN Electronic Journal;2024

4. Real-time botnet detection on large network bandwidths using machine learning;Scientific Reports;2023-03-15

5. BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning;Computers, Materials & Continua;2023