Unsupervised detection of botnet activities using frequent pattern tree mining
Open Access
- 25 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Complex & Intelligent Systems
- Vol. 8 (2), 761-769
- https://doi.org/10.1007/s40747-021-00281-5
Abstract
No abstract availableFunding Information
- Double First Class University Plan (3307012001A)
- Natural Science Foundation of China (62073074)
This publication has 28 references indexed in Scilit:
- A Comprehensive Study of Email Spam Botnet DetectionIEEE Communications Surveys & Tutorials, 2015
- Behavioral fine-grained detection and classification of P2P botsJournal of Computer Virology and Hacking Techniques, 2014
- PeerShark: flow-clustering and conversation-generation for malicious peer-to-peer traffic identificationEURASIP Journal on Information Security, 2014
- PeerRush: Mining for unwanted P2P trafficJournal of Information Security and Applications, 2014
- PeerShark: Detecting Peer-to-Peer Botnets by Tracking ConversationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Botnet detection based on traffic behavior analysis and flow intervalsComputers & Security, 2013
- Performance Evaluation of Apriori and FP-Growth AlgorithmsInternational Journal of Computer Applications, 2013
- Detecting P2P bots by mining the regional periodicityJournal of Zhejiang University SCIENCE C, 2013
- Detecting P2P botnets through network behavior analysis and machine learningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- MapReduceCommunications of the ACM, 2008