International Journal of Electrical, Electronics and Data Communication (IJEEDC)
eISSN:2320-2084 , pISSN:2321-2950
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Statistics report
Jan. 2021
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 93
Paper Published : 1465
No. of Authors : 3971
  Journal Paper

Paper Title
Peer-To-Peer Botnet: Analysis of Botnet Detection Techniques using Machine Learning Algorithms

The collection of several infectious bots together running over an infected software forms the botnet. The botnet have the centralized command and control architecture but these system has a drawback of complete shutdown of the system when default occurs, this is the single point failure in the botnet. To overcome this fault decentralized peer-to-peer botnet have been introduced. In this paper we review different approaches to detect peer-to-peer botnet and the machine learning algorithms they had used in their work. We also compared the accuracy of different models used by other authors for the detecting botnet. Keywords - Botnet, Peer-To-Peer, Detection, Machine Learning, Support Vector Machine, Decision Tree.

Author - Shabnam, Aditya K Saxena, Chirag Joshi

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| Published on 2019-09-24
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