International Journal of Electrical, Electronics and Data Communication (IJEEDC)
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Statistics report
Apr. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 133
Paper Published : 1712
No. of Authors : 4737
  Journal Paper


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

Author :Shabnam, Aditya K Saxena, Chirag Joshi

Article Citation :Shabnam ,Aditya K Saxena ,Chirag Joshi , (2019 ) " Peer-To-Peer Botnet: Analysis of Botnet Detection Techniques using Machine Learning Algorithms " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 19-25, Volume-7,Issue-8

Abstract : 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.

Type : Research paper

Published : Volume-7,Issue-8


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