A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection
Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Or...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qu, Xiaofei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Hierarchical self-organizing map (HSOM) |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Mobile networks and applications - Springer US, 1996, 26(2019), 2 vom: 02. Okt., Seite 808-829 |
|---|---|
| Übergeordnetes Werk: |
volume:26 ; year:2019 ; number:2 ; day:02 ; month:10 ; pages:808-829 |
| Links: |
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| DOI / URN: |
10.1007/s11036-019-01353-0 |
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OLC2125370654 |
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| 520 | |a Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. | ||
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10.1007/s11036-019-01353-0 doi (DE-627)OLC2125370654 (DE-He213)s11036-019-01353-0-p DE-627 ger DE-627 rakwb eng 004 VZ Qu, Xiaofei verfasserin aut A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. Self organizing map (SOM) Hierarchical self-organizing map (HSOM) Growing hierarchical self-organizing map (GHSOM) Intrusion detection system (IDS) Yang, Lin aut Guo, Kai aut Ma, Linru aut Sun, Meng aut Ke, Mingxing aut Li, Mu aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 02. Okt., Seite 808-829 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:02 month:10 pages:808-829 https://doi.org/10.1007/s11036-019-01353-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 02 10 808-829 |
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10.1007/s11036-019-01353-0 doi (DE-627)OLC2125370654 (DE-He213)s11036-019-01353-0-p DE-627 ger DE-627 rakwb eng 004 VZ Qu, Xiaofei verfasserin aut A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. Self organizing map (SOM) Hierarchical self-organizing map (HSOM) Growing hierarchical self-organizing map (GHSOM) Intrusion detection system (IDS) Yang, Lin aut Guo, Kai aut Ma, Linru aut Sun, Meng aut Ke, Mingxing aut Li, Mu aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 02. Okt., Seite 808-829 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:02 month:10 pages:808-829 https://doi.org/10.1007/s11036-019-01353-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 02 10 808-829 |
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10.1007/s11036-019-01353-0 doi (DE-627)OLC2125370654 (DE-He213)s11036-019-01353-0-p DE-627 ger DE-627 rakwb eng 004 VZ Qu, Xiaofei verfasserin aut A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. Self organizing map (SOM) Hierarchical self-organizing map (HSOM) Growing hierarchical self-organizing map (GHSOM) Intrusion detection system (IDS) Yang, Lin aut Guo, Kai aut Ma, Linru aut Sun, Meng aut Ke, Mingxing aut Li, Mu aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 02. Okt., Seite 808-829 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:02 month:10 pages:808-829 https://doi.org/10.1007/s11036-019-01353-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 02 10 808-829 |
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10.1007/s11036-019-01353-0 doi (DE-627)OLC2125370654 (DE-He213)s11036-019-01353-0-p DE-627 ger DE-627 rakwb eng 004 VZ Qu, Xiaofei verfasserin aut A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. Self organizing map (SOM) Hierarchical self-organizing map (HSOM) Growing hierarchical self-organizing map (GHSOM) Intrusion detection system (IDS) Yang, Lin aut Guo, Kai aut Ma, Linru aut Sun, Meng aut Ke, Mingxing aut Li, Mu aut Enthalten in Mobile networks and applications Springer US, 1996 26(2019), 2 vom: 02. Okt., Seite 808-829 (DE-627)215279522 (DE-600)1342049-5 (DE-576)063244756 1383-469X nnns volume:26 year:2019 number:2 day:02 month:10 pages:808-829 https://doi.org/10.1007/s11036-019-01353-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 26 2019 2 02 10 808-829 |
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A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection |
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Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstractGer |
Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract This paper describes a focused literature survey of self-organizing maps (SOM) in support of intrusion detection. Specifically, the SOM architecture can be divided into two categories, i.e., static-layered architectures and dynamic-layered architectures. The former one, Hierarchical Self-Organizing Maps (HSOM), can effectively reduce the computational overheads and efficiently represent the hierarchy of data. The latter one, Growing Hierarchical Self-Organizing Maps (GHSOM), is quite effective for online intrusion detection with low computing latency, dynamic self-adaptability, and self-learning. The ultimate goal of SOM architecture is to accurately represent the topological relationship of data to identify any anomalous attack. The overall goal of this survey is to comprehensively compare the primitive components and properties of SOM-based intrusion detection. By comparing with the two SOM-based intrusion detection systems, we can clearly understand the existing challenges of SOM-based intrusion detection systems and indicate the future research directions. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| title_short |
A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection |
| url |
https://doi.org/10.1007/s11036-019-01353-0 |
| remote_bool |
false |
| author2 |
Yang, Lin Guo, Kai Ma, Linru Sun, Meng Ke, Mingxing Li, Mu |
| author2Str |
Yang, Lin Guo, Kai Ma, Linru Sun, Meng Ke, Mingxing Li, Mu |
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215279522 |
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| doi_str |
10.1007/s11036-019-01353-0 |
| up_date |
2024-07-04T03:27:46.020Z |
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7.4006386 |