The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review
In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Ar...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2012/850160 |
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| _version_ | 1850217313986412544 |
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| author | Gulshan Kumar Krishan Kumar |
| author_facet | Gulshan Kumar Krishan Kumar |
| author_sort | Gulshan Kumar |
| collection | DOAJ |
| description | In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs). |
| format | Article |
| id | doaj-art-3faca4bddeed4fb5b1429e3bc83e4ece |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-3faca4bddeed4fb5b1429e3bc83e4ece2025-08-20T02:08:04ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322012-01-01201210.1155/2012/850160850160The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A ReviewGulshan Kumar0Krishan Kumar1Department of Computer Application, Shaheed Bhagat Singh State Technical Campus, Ferozepur, Punjab 152004, IndiaDepartment of Computer Science & Engineering, Punjab Institute of Technology, Kapurthala, Punjab 144601, IndiaIn supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).http://dx.doi.org/10.1155/2012/850160 |
| spellingShingle | Gulshan Kumar Krishan Kumar The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review Applied Computational Intelligence and Soft Computing |
| title | The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review |
| title_full | The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review |
| title_fullStr | The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review |
| title_full_unstemmed | The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review |
| title_short | The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review |
| title_sort | use of artificial intelligence based ensembles for intrusion detection a review |
| url | http://dx.doi.org/10.1155/2012/850160 |
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