Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern

Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios,...
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Autor*in:	Ying Zhao [verfasserIn] Junjun Chen [verfasserIn] Di Wu [verfasserIn] Jian Teng [verfasserIn] Nabin Sharma [verfasserIn] Atul Sajjanhar [verfasserIn] Michael Blumenstein [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	anomaly detection frequent pattern user behavior

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 10(2019), 8, p 262
Übergeordnetes Werk:	volume:10 ; year:2019 ; number:8, p 262

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info10080262

Katalog-ID:	DOAJ041357051

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520			\|a Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
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spelling	10.3390/info10080262 doi (DE-627)DOAJ041357051 (DE-599)DOAJ67545aac809d40a0be9f1c024b11d7db DE-627 ger DE-627 rakwb eng T58.5-58.64 Ying Zhao verfasserin aut Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods. anomaly detection frequent pattern user behavior Information technology Junjun Chen verfasserin aut Di Wu verfasserin aut Jian Teng verfasserin aut Nabin Sharma verfasserin aut Atul Sajjanhar verfasserin aut Michael Blumenstein verfasserin aut In Information MDPI AG, 2010 10(2019), 8, p 262 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:10 year:2019 number:8, p 262 https://doi.org/10.3390/info10080262 kostenfrei https://doaj.org/article/67545aac809d40a0be9f1c024b11d7db kostenfrei https://www.mdpi.com/2078-2489/10/8/262 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 10 2019 8, p 262
allfields_unstemmed	10.3390/info10080262 doi (DE-627)DOAJ041357051 (DE-599)DOAJ67545aac809d40a0be9f1c024b11d7db DE-627 ger DE-627 rakwb eng T58.5-58.64 Ying Zhao verfasserin aut Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods. anomaly detection frequent pattern user behavior Information technology Junjun Chen verfasserin aut Di Wu verfasserin aut Jian Teng verfasserin aut Nabin Sharma verfasserin aut Atul Sajjanhar verfasserin aut Michael Blumenstein verfasserin aut In Information MDPI AG, 2010 10(2019), 8, p 262 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:10 year:2019 number:8, p 262 https://doi.org/10.3390/info10080262 kostenfrei https://doaj.org/article/67545aac809d40a0be9f1c024b11d7db kostenfrei https://www.mdpi.com/2078-2489/10/8/262 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 10 2019 8, p 262
allfieldsGer	10.3390/info10080262 doi (DE-627)DOAJ041357051 (DE-599)DOAJ67545aac809d40a0be9f1c024b11d7db DE-627 ger DE-627 rakwb eng T58.5-58.64 Ying Zhao verfasserin aut Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods. anomaly detection frequent pattern user behavior Information technology Junjun Chen verfasserin aut Di Wu verfasserin aut Jian Teng verfasserin aut Nabin Sharma verfasserin aut Atul Sajjanhar verfasserin aut Michael Blumenstein verfasserin aut In Information MDPI AG, 2010 10(2019), 8, p 262 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:10 year:2019 number:8, p 262 https://doi.org/10.3390/info10080262 kostenfrei https://doaj.org/article/67545aac809d40a0be9f1c024b11d7db kostenfrei https://www.mdpi.com/2078-2489/10/8/262 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 10 2019 8, p 262
allfieldsSound	10.3390/info10080262 doi (DE-627)DOAJ041357051 (DE-599)DOAJ67545aac809d40a0be9f1c024b11d7db DE-627 ger DE-627 rakwb eng T58.5-58.64 Ying Zhao verfasserin aut Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods. anomaly detection frequent pattern user behavior Information technology Junjun Chen verfasserin aut Di Wu verfasserin aut Jian Teng verfasserin aut Nabin Sharma verfasserin aut Atul Sajjanhar verfasserin aut Michael Blumenstein verfasserin aut In Information MDPI AG, 2010 10(2019), 8, p 262 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:10 year:2019 number:8, p 262 https://doi.org/10.3390/info10080262 kostenfrei https://doaj.org/article/67545aac809d40a0be9f1c024b11d7db kostenfrei https://www.mdpi.com/2078-2489/10/8/262 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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 10 2019 8, p 262
language	English
source	In Information 10(2019), 8, p 262 volume:10 year:2019 number:8, p 262
sourceStr	In Information 10(2019), 8, p 262 volume:10 year:2019 number:8, p 262
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institution	findex.gbv.de
topic_facet	anomaly detection frequent pattern user behavior Information technology
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container_title	Information
authorswithroles_txt_mv	Ying Zhao @@aut@@ Junjun Chen @@aut@@ Di Wu @@aut@@ Jian Teng @@aut@@ Nabin Sharma @@aut@@ Atul Sajjanhar @@aut@@ Michael Blumenstein @@aut@@
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callnumber-first	T - Technology
author	Ying Zhao
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topic_title	T58.5-58.64 Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern anomaly detection frequent pattern user behavior
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title	Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern
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title_auth	Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern
abstract	Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
abstractGer	Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
abstract_unstemmed	Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
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