Unsupervised Learning: Using Clustering Algorithms to Detect Peer to Peer Botnet Flows

Andrea E. Medina Paredes; Hung-Min Sun

doi:10.1007/978-3-030-46828-6_26

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Conference paper

Unsupervised Learning: Using Clustering Algorithms to Detect Peer to Peer Botnet Flows

Andrea E. Medina Paredes and Hung-Min Sun

Advances in Intelligent Systems and Computing, Vol.1145 AISC, pp.299-311

2020

DOI: https://doi.org/10.1007/978-3-030-46828-6_26

Abstract

Clusters

Network flows

P2P botnets

Unsupervised learning

Control and Systems Engineering

Computer Science (all)

The war against botnet infection is fought every day by users that want to feel safe against any threat of compromise hosts. In this paper we are going to focus on the behavior of Peer 2 Peer (P2P) botnets, which along with hybrid botnets is a growing trend among attackers. The main approach will consist of a behavior comparison among features extracted from network flows, focusing only in the flows from P2P applications including P2P botnets.

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Title: Unsupervised Learning: Using Clustering Algorithms to Detect Peer to Peer Botnet Flows
Translated title: Unsupervised Learning: Using Clustering Algorithms to Detect Peer to Peer Botnet Flows
Creators - without role: Andrea E. Medina Paredes - National Tsing Hua University
Hung-Min Sun - Department of Computer Science , National Tsing Hua University
Publication Details: Advances in Intelligent Systems and Computing, Vol.1145 AISC, pp.299-311
Identifiers: 9783030468279; 9957770478606774
Academic Unit: Department of Computer Science, College of Electrical Engineering and Computer Science, National Tsing Hua University
Language: English
Resource Type: Conference paper

Unsupervised Learning: Using Clustering Algorithms to Detect Peer to Peer Botnet Flows

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