MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review

Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the netw...
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Autor*in:	Kumar, Gulshan [verfasserIn] Thakur, Kutub [verfasserIn] Ayyagari, Maruthi Rohit [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Artificial intelligence Ensemble Hybrid classifiers Intrusion detection Machine learning

Übergeordnetes Werk:	Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 76(2020), 11 vom: 12. Feb., Seite 8938-8971
Übergeordnetes Werk:	volume:76 ; year:2020 ; number:11 ; day:12 ; month:02 ; pages:8938-8971

Links:	Volltext

DOI / URN:	10.1007/s11227-020-03196-z

Katalog-ID:	SPR040959147

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520			\|a Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs.
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allfields	10.1007/s11227-020-03196-z doi (DE-627)SPR040959147 (SPR)s11227-020-03196-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kumar, Gulshan verfasserin aut MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs. Artificial intelligence (dpeaa)DE-He213 Ensemble (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Intrusion detection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Thakur, Kutub verfasserin aut Ayyagari, Maruthi Rohit verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 11 vom: 12. Feb., Seite 8938-8971 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:11 day:12 month:02 pages:8938-8971 https://dx.doi.org/10.1007/s11227-020-03196-z 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_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 11 12 02 8938-8971
spelling	10.1007/s11227-020-03196-z doi (DE-627)SPR040959147 (SPR)s11227-020-03196-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kumar, Gulshan verfasserin aut MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs. Artificial intelligence (dpeaa)DE-He213 Ensemble (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Intrusion detection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Thakur, Kutub verfasserin aut Ayyagari, Maruthi Rohit verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 11 vom: 12. Feb., Seite 8938-8971 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:11 day:12 month:02 pages:8938-8971 https://dx.doi.org/10.1007/s11227-020-03196-z 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_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 11 12 02 8938-8971
allfields_unstemmed	10.1007/s11227-020-03196-z doi (DE-627)SPR040959147 (SPR)s11227-020-03196-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kumar, Gulshan verfasserin aut MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs. Artificial intelligence (dpeaa)DE-He213 Ensemble (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Intrusion detection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Thakur, Kutub verfasserin aut Ayyagari, Maruthi Rohit verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 11 vom: 12. Feb., Seite 8938-8971 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:11 day:12 month:02 pages:8938-8971 https://dx.doi.org/10.1007/s11227-020-03196-z 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_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 11 12 02 8938-8971
allfieldsGer	10.1007/s11227-020-03196-z doi (DE-627)SPR040959147 (SPR)s11227-020-03196-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kumar, Gulshan verfasserin aut MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs. Artificial intelligence (dpeaa)DE-He213 Ensemble (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Intrusion detection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Thakur, Kutub verfasserin aut Ayyagari, Maruthi Rohit verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 11 vom: 12. Feb., Seite 8938-8971 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:11 day:12 month:02 pages:8938-8971 https://dx.doi.org/10.1007/s11227-020-03196-z 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_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 11 12 02 8938-8971
allfieldsSound	10.1007/s11227-020-03196-z doi (DE-627)SPR040959147 (SPR)s11227-020-03196-z-e DE-627 ger DE-627 rakwb eng 004 620 ASE 54.20 bkl Kumar, Gulshan verfasserin aut MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs. Artificial intelligence (dpeaa)DE-He213 Ensemble (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Intrusion detection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Thakur, Kutub verfasserin aut Ayyagari, Maruthi Rohit verfasserin aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 76(2020), 11 vom: 12. Feb., Seite 8938-8971 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:76 year:2020 number:11 day:12 month:02 pages:8938-8971 https://dx.doi.org/10.1007/s11227-020-03196-z 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_206 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.20 ASE AR 76 2020 11 12 02 8938-8971
language	English
source	Enthalten in The journal of supercomputing 76(2020), 11 vom: 12. Feb., Seite 8938-8971 volume:76 year:2020 number:11 day:12 month:02 pages:8938-8971
sourceStr	Enthalten in The journal of supercomputing 76(2020), 11 vom: 12. Feb., Seite 8938-8971 volume:76 year:2020 number:11 day:12 month:02 pages:8938-8971
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial intelligence Ensemble Hybrid classifiers Intrusion detection Machine learning
dewey-raw	004
isfreeaccess_bool	false
container_title	The journal of supercomputing
authorswithroles_txt_mv	Kumar, Gulshan @@aut@@ Thakur, Kutub @@aut@@ Ayyagari, Maruthi Rohit @@aut@@
publishDateDaySort_date	2020-02-12T00:00:00Z
hierarchy_top_id	271350202
dewey-sort	14
id	SPR040959147
language_de	englisch
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author	Kumar, Gulshan
spellingShingle	Kumar, Gulshan ddc 004 bkl 54.20 misc Artificial intelligence misc Ensemble misc Hybrid classifiers misc Intrusion detection misc Machine learning MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review
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topic_title	004 620 ASE 54.20 bkl MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review Artificial intelligence (dpeaa)DE-He213 Ensemble (dpeaa)DE-He213 Hybrid classifiers (dpeaa)DE-He213 Intrusion detection (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213
topic	ddc 004 bkl 54.20 misc Artificial intelligence misc Ensemble misc Hybrid classifiers misc Intrusion detection misc Machine learning
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title	MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review
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title_full	MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review
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title_sort	mlesidss: machine learning-based ensembles for intrusion detection systems—a review
title_auth	MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review
abstract	Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs.
abstractGer	Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs.
abstract_unstemmed	Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs.
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title_short	MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review
url	https://dx.doi.org/10.1007/s11227-020-03196-z
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author2	Thakur, Kutub Ayyagari, Maruthi Rohit
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up_date	2024-07-03T19:21:50.263Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR040959147</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111054426.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-020-03196-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR040959147</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11227-020-03196-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kumar, Gulshan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources.With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. 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