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Towards Accurate Node-Based Detection of P2P Botnets
Botnets are a serious security threat to the current Internet infrastructure. In this paper, we propose a novel direction for P2P botnet detection called node-based detection. This approach focuses on the network characteristics of individual nodes. Based on our model, we examine node's flows a...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Hindawi Publishing Corporation
2014
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4095737/ https://www.ncbi.nlm.nih.gov/pubmed/25089287 http://dx.doi.org/10.1155/2014/425491 |
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author | Yin, Chunyong |
author_facet | Yin, Chunyong |
author_sort | Yin, Chunyong |
collection | PubMed |
description | Botnets are a serious security threat to the current Internet infrastructure. In this paper, we propose a novel direction for P2P botnet detection called node-based detection. This approach focuses on the network characteristics of individual nodes. Based on our model, we examine node's flows and extract the useful features over a given time period. We have tested our approach on real-life data sets and achieved detection rates of 99-100% and low false positives rates of 0–2%. Comparison with other similar approaches on the same data sets shows that our approach outperforms the existing approaches. |
format | Online Article Text |
id | pubmed-4095737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-40957372014-08-03 Towards Accurate Node-Based Detection of P2P Botnets Yin, Chunyong ScientificWorldJournal Research Article Botnets are a serious security threat to the current Internet infrastructure. In this paper, we propose a novel direction for P2P botnet detection called node-based detection. This approach focuses on the network characteristics of individual nodes. Based on our model, we examine node's flows and extract the useful features over a given time period. We have tested our approach on real-life data sets and achieved detection rates of 99-100% and low false positives rates of 0–2%. Comparison with other similar approaches on the same data sets shows that our approach outperforms the existing approaches. Hindawi Publishing Corporation 2014 2014-06-24 /pmc/articles/PMC4095737/ /pubmed/25089287 http://dx.doi.org/10.1155/2014/425491 Text en Copyright © 2014 Chunyong Yin. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yin, Chunyong Towards Accurate Node-Based Detection of P2P Botnets |
title | Towards Accurate Node-Based Detection of P2P Botnets |
title_full | Towards Accurate Node-Based Detection of P2P Botnets |
title_fullStr | Towards Accurate Node-Based Detection of P2P Botnets |
title_full_unstemmed | Towards Accurate Node-Based Detection of P2P Botnets |
title_short | Towards Accurate Node-Based Detection of P2P Botnets |
title_sort | towards accurate node-based detection of p2p botnets |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4095737/ https://www.ncbi.nlm.nih.gov/pubmed/25089287 http://dx.doi.org/10.1155/2014/425491 |
work_keys_str_mv | AT yinchunyong towardsaccuratenodebaseddetectionofp2pbotnets |