A distance sum-based hybrid method for intrusion detection
Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Guo, Chun [verfasserIn] Zhou, Yajian [verfasserIn] Ping, Yuan [verfasserIn] Zhang, Zhongkun [verfasserIn] Liu, Guole [verfasserIn] Yang, Yixian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 40(2013), 1 vom: 13. Juni, Seite 178-188 |
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| Übergeordnetes Werk: |
volume:40 ; year:2013 ; number:1 ; day:13 ; month:06 ; pages:178-188 |
| Links: |
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| DOI / URN: |
10.1007/s10489-013-0452-6 |
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| Katalog-ID: |
SPR01043674X |
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| 520 | |a Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. | ||
| 650 | 4 | |a Intrusion detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hybrid classifiers |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Euclidean distance function |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Pattern recognition |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Zhou, Yajian |e verfasserin |4 aut | |
| 700 | 1 | |a Ping, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhongkun |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Guole |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yixian |e verfasserin |4 aut | |
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10.1007/s10489-013-0452-6 doi (DE-627)SPR01043674X (SPR)s10489-013-0452-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Guo, Chun verfasserin aut A distance sum-based hybrid method for intrusion detection 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. Intrusion detection (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance function (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Zhou, Yajian verfasserin aut Ping, Yuan verfasserin aut Zhang, Zhongkun verfasserin aut Liu, Guole verfasserin aut Yang, Yixian verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 40(2013), 1 vom: 13. Juni, Seite 178-188 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:40 year:2013 number:1 day:13 month:06 pages:178-188 https://dx.doi.org/10.1007/s10489-013-0452-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 40 2013 1 13 06 178-188 |
| spelling |
10.1007/s10489-013-0452-6 doi (DE-627)SPR01043674X (SPR)s10489-013-0452-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Guo, Chun verfasserin aut A distance sum-based hybrid method for intrusion detection 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. Intrusion detection (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance function (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Zhou, Yajian verfasserin aut Ping, Yuan verfasserin aut Zhang, Zhongkun verfasserin aut Liu, Guole verfasserin aut Yang, Yixian verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 40(2013), 1 vom: 13. Juni, Seite 178-188 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:40 year:2013 number:1 day:13 month:06 pages:178-188 https://dx.doi.org/10.1007/s10489-013-0452-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 40 2013 1 13 06 178-188 |
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10.1007/s10489-013-0452-6 doi (DE-627)SPR01043674X (SPR)s10489-013-0452-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Guo, Chun verfasserin aut A distance sum-based hybrid method for intrusion detection 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. Intrusion detection (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance function (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Zhou, Yajian verfasserin aut Ping, Yuan verfasserin aut Zhang, Zhongkun verfasserin aut Liu, Guole verfasserin aut Yang, Yixian verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 40(2013), 1 vom: 13. Juni, Seite 178-188 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:40 year:2013 number:1 day:13 month:06 pages:178-188 https://dx.doi.org/10.1007/s10489-013-0452-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 40 2013 1 13 06 178-188 |
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10.1007/s10489-013-0452-6 doi (DE-627)SPR01043674X (SPR)s10489-013-0452-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Guo, Chun verfasserin aut A distance sum-based hybrid method for intrusion detection 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. Intrusion detection (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance function (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Zhou, Yajian verfasserin aut Ping, Yuan verfasserin aut Zhang, Zhongkun verfasserin aut Liu, Guole verfasserin aut Yang, Yixian verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 40(2013), 1 vom: 13. Juni, Seite 178-188 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:40 year:2013 number:1 day:13 month:06 pages:178-188 https://dx.doi.org/10.1007/s10489-013-0452-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 40 2013 1 13 06 178-188 |
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10.1007/s10489-013-0452-6 doi (DE-627)SPR01043674X (SPR)s10489-013-0452-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 30.20 bkl Guo, Chun verfasserin aut A distance sum-based hybrid method for intrusion detection 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. Intrusion detection (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance function (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 Zhou, Yajian verfasserin aut Ping, Yuan verfasserin aut Zhang, Zhongkun verfasserin aut Liu, Guole verfasserin aut Yang, Yixian verfasserin aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 40(2013), 1 vom: 13. Juni, Seite 178-188 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:40 year:2013 number:1 day:13 month:06 pages:178-188 https://dx.doi.org/10.1007/s10489-013-0452-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 30.20 ASE AR 40 2013 1 13 06 178-188 |
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Enthalten in Applied intelligence 40(2013), 1 vom: 13. Juni, Seite 178-188 volume:40 year:2013 number:1 day:13 month:06 pages:178-188 |
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Intrusion detection Hybrid classifiers Support vector machine Euclidean distance function Pattern recognition |
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Applied intelligence |
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Guo, Chun @@aut@@ Zhou, Yajian @@aut@@ Ping, Yuan @@aut@@ Zhang, Zhongkun @@aut@@ Liu, Guole @@aut@@ Yang, Yixian @@aut@@ |
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Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. 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004 ASE 54.72 bkl 30.20 bkl A distance sum-based hybrid method for intrusion detection Intrusion detection (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Support vector machine (dpeaa)DE-He213 Euclidean distance function (dpeaa)DE-He213 Pattern recognition (dpeaa)DE-He213 |
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ddc 004 bkl 54.72 bkl 30.20 misc Intrusion detection misc Hybrid classifiers misc Support vector machine misc Euclidean distance function misc Pattern recognition |
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distance sum-based hybrid method for intrusion detection |
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A distance sum-based hybrid method for intrusion detection |
| abstract |
Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. |
| abstractGer |
Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. |
| abstract_unstemmed |
Abstract Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems. |
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| title_short |
A distance sum-based hybrid method for intrusion detection |
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https://dx.doi.org/10.1007/s10489-013-0452-6 |
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true |
| author2 |
Zhou, Yajian Ping, Yuan Zhang, Zhongkun Liu, Guole Yang, Yixian |
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Zhou, Yajian Ping, Yuan Zhang, Zhongkun Liu, Guole Yang, Yixian |
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271180919 |
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| doi_str |
10.1007/s10489-013-0452-6 |
| up_date |
2024-07-03T16:06:14.850Z |
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| score |
7.400939 |