Unsupervised detection of botnet activities using frequent pattern tree mining

Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major c...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hao, Siqiang [verfasserIn] Liu, Di Baldi, Simone Yu, Wenwu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Botnet detection Internet security Frequent pattern tree Data mining

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: Complex & intelligent systems - Berlin : SpringerOpen, 2015, 8(2021), 2 vom: 25. Feb., Seite 761-769
Übergeordnetes Werk:	volume:8 ; year:2021 ; number:2 ; day:25 ; month:02 ; pages:761-769

Links:	Volltext

DOI / URN:	10.1007/s40747-021-00281-5

Katalog-ID:	SPR046882839

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allfields	10.1007/s40747-021-00281-5 doi (DE-627)SPR046882839 (SPR)s40747-021-00281-5-e DE-627 ger DE-627 rakwb eng Hao, Siqiang verfasserin aut Unsupervised detection of botnet activities using frequent pattern tree mining 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. Botnet detection (dpeaa)DE-He213 Internet security (dpeaa)DE-He213 Frequent pattern tree (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Liu, Di aut Baldi, Simone aut Yu, Wenwu (orcid)0000-0003-3755-179X aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2021), 2 vom: 25. Feb., Seite 761-769 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2021 number:2 day:25 month:02 pages:761-769 https://dx.doi.org/10.1007/s40747-021-00281-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 2 25 02 761-769
spelling	10.1007/s40747-021-00281-5 doi (DE-627)SPR046882839 (SPR)s40747-021-00281-5-e DE-627 ger DE-627 rakwb eng Hao, Siqiang verfasserin aut Unsupervised detection of botnet activities using frequent pattern tree mining 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. Botnet detection (dpeaa)DE-He213 Internet security (dpeaa)DE-He213 Frequent pattern tree (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Liu, Di aut Baldi, Simone aut Yu, Wenwu (orcid)0000-0003-3755-179X aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2021), 2 vom: 25. Feb., Seite 761-769 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2021 number:2 day:25 month:02 pages:761-769 https://dx.doi.org/10.1007/s40747-021-00281-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 2 25 02 761-769
allfields_unstemmed	10.1007/s40747-021-00281-5 doi (DE-627)SPR046882839 (SPR)s40747-021-00281-5-e DE-627 ger DE-627 rakwb eng Hao, Siqiang verfasserin aut Unsupervised detection of botnet activities using frequent pattern tree mining 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. Botnet detection (dpeaa)DE-He213 Internet security (dpeaa)DE-He213 Frequent pattern tree (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Liu, Di aut Baldi, Simone aut Yu, Wenwu (orcid)0000-0003-3755-179X aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2021), 2 vom: 25. Feb., Seite 761-769 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2021 number:2 day:25 month:02 pages:761-769 https://dx.doi.org/10.1007/s40747-021-00281-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 2 25 02 761-769
allfieldsGer	10.1007/s40747-021-00281-5 doi (DE-627)SPR046882839 (SPR)s40747-021-00281-5-e DE-627 ger DE-627 rakwb eng Hao, Siqiang verfasserin aut Unsupervised detection of botnet activities using frequent pattern tree mining 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. Botnet detection (dpeaa)DE-He213 Internet security (dpeaa)DE-He213 Frequent pattern tree (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Liu, Di aut Baldi, Simone aut Yu, Wenwu (orcid)0000-0003-3755-179X aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2021), 2 vom: 25. Feb., Seite 761-769 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2021 number:2 day:25 month:02 pages:761-769 https://dx.doi.org/10.1007/s40747-021-00281-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 2 25 02 761-769
allfieldsSound	10.1007/s40747-021-00281-5 doi (DE-627)SPR046882839 (SPR)s40747-021-00281-5-e DE-627 ger DE-627 rakwb eng Hao, Siqiang verfasserin aut Unsupervised detection of botnet activities using frequent pattern tree mining 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. Botnet detection (dpeaa)DE-He213 Internet security (dpeaa)DE-He213 Frequent pattern tree (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Liu, Di aut Baldi, Simone aut Yu, Wenwu (orcid)0000-0003-3755-179X aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 8(2021), 2 vom: 25. Feb., Seite 761-769 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:8 year:2021 number:2 day:25 month:02 pages:761-769 https://dx.doi.org/10.1007/s40747-021-00281-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 2 25 02 761-769
language	English
source	Enthalten in Complex & intelligent systems 8(2021), 2 vom: 25. Feb., Seite 761-769 volume:8 year:2021 number:2 day:25 month:02 pages:761-769
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topic_facet	Botnet detection Internet security Frequent pattern tree Data mining
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authorswithroles_txt_mv	Hao, Siqiang @@aut@@ Liu, Di @@aut@@ Baldi, Simone @@aut@@ Yu, Wenwu @@aut@@
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author	Hao, Siqiang
spellingShingle	Hao, Siqiang misc Botnet detection misc Internet security misc Frequent pattern tree misc Data mining Unsupervised detection of botnet activities using frequent pattern tree mining
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topic_title	Unsupervised detection of botnet activities using frequent pattern tree mining Botnet detection (dpeaa)DE-He213 Internet security (dpeaa)DE-He213 Frequent pattern tree (dpeaa)DE-He213 Data mining (dpeaa)DE-He213
topic	misc Botnet detection misc Internet security misc Frequent pattern tree misc Data mining
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title	Unsupervised detection of botnet activities using frequent pattern tree mining
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title_full	Unsupervised detection of botnet activities using frequent pattern tree mining
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title_sort	unsupervised detection of botnet activities using frequent pattern tree mining
title_auth	Unsupervised detection of botnet activities using frequent pattern tree mining
abstract	Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. © The Author(s) 2021
abstractGer	Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. © The Author(s) 2021
abstract_unstemmed	Abstract A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-service attacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadily increased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and more difficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and many others). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field of data mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item mining algorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysis of rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervised fashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peer host PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of the proposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature. © The Author(s) 2021
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title_short	Unsupervised detection of botnet activities using frequent pattern tree mining
url	https://dx.doi.org/10.1007/s40747-021-00281-5
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