Feature selection using cloud-based parallel genetic algorithm for intrusion detection data classification
With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. On...
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| Published in: | Neural computing & applications Vol. 33; no. 18; pp. 11861 - 11873 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer London
01.09.2021
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| Abstract | With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduction using feature selection methods; however, the processing time needed for these methods is a major drawback. This study aims at mitigating this problem by migrating the algorithm to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units. The genetic algorithm-based methods were put in the focus of this study. Hadoop, an open-source MapReduce library, was used as a framework for implementing parallel genetic algorithms within our research. The representative machine learning methods, SVM (support vector machine), ANN (artificial neural network), RT (random tree), logistic regression and Naive Bayes, were embedded into implementation for feature selection. The feature selection methods were applied to four NSL-KDD data sets, and the number of features is reduced from cca 40 to cca 10 data sets with the accuracy of 90.45%. These results have both significant practical and theoretical impact. On the one hand, the genetic algorithm has been parallelized in the MapReduce manner, which has been considered unachievable in a strict sense. Furthermore, the genetic algorithm allows randomness-enhanced feature selection and its parallelization reduces overall data preprocessing and allows larger population count which in turn leads to better feature selection. On the practical side, it has been shown that this implementation outperforms the existing feature selection methods. |
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| AbstractList | With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduction using feature selection methods; however, the processing time needed for these methods is a major drawback. This study aims at mitigating this problem by migrating the algorithm to a MapReduce implementation suitable for parallelization on a high number of commodity hardware units. The genetic algorithm-based methods were put in the focus of this study. Hadoop, an open-source MapReduce library, was used as a framework for implementing parallel genetic algorithms within our research. The representative machine learning methods, SVM (support vector machine), ANN (artificial neural network), RT (random tree), logistic regression and Naive Bayes, were embedded into implementation for feature selection. The feature selection methods were applied to four NSL-KDD data sets, and the number of features is reduced from cca 40 to cca 10 data sets with the accuracy of 90.45%. These results have both significant practical and theoretical impact. On the one hand, the genetic algorithm has been parallelized in the MapReduce manner, which has been considered unachievable in a strict sense. Furthermore, the genetic algorithm allows randomness-enhanced feature selection and its parallelization reduces overall data preprocessing and allows larger population count which in turn leads to better feature selection. On the practical side, it has been shown that this implementation outperforms the existing feature selection methods. |
| Author | Mehanović, Dželila Jukić, Samed Kečo, Dino Miljković, Adnan Kevrić, Jasmin Mašetić, Zerina |
| Author_xml | – sequence: 1 givenname: Dželila orcidid: 0000-0001-7731-0478 surname: Mehanović fullname: Mehanović, Dželila email: dzelila.mehanovic@ibu.edu.ba organization: Faculty of Engineering and Natural Sciences, International Burch University – sequence: 2 givenname: Dino surname: Kečo fullname: Kečo, Dino organization: Faculty of Engineering and Natural Sciences, International Burch University – sequence: 3 givenname: Jasmin surname: Kevrić fullname: Kevrić, Jasmin organization: Faculty of Engineering and Natural Sciences, International Burch University – sequence: 4 givenname: Samed surname: Jukić fullname: Jukić, Samed organization: Faculty of Engineering and Natural Sciences, International Burch University – sequence: 5 givenname: Adnan surname: Miljković fullname: Miljković, Adnan organization: Faculty of Engineering and Natural Sciences, International Burch University – sequence: 6 givenname: Zerina surname: Mašetić fullname: Mašetić, Zerina organization: Faculty of Engineering and Natural Sciences, International Burch University |
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| Cites_doi | 10.6028/nist.sp.800-94 10.1080/19942060.2018.1452296 10.21533/scjournal.v1i2.61 10.1007/s00521-016-2418-1 10.1109/access.2020.2972627 10.1145/1327452.1327492 10.1016/j.aquaeng.2020.102053 10.1016/j.neucom.2020.08.063 10.1007/978-981-32-9343-4_16 10.1016/j.eswa.2017.04.017 10.1016/j.engappai.2012.05.023 10.1016/j.asoc.2009.06.019 10.1109/access.2019.2951750 10.1002/sec.403 10.1016/j.ymssp.2020.107353 10.1007/s00521-016-2780-z 10.1145/382912.382923 10.1016/j.jfranklin.2020.04.024 10.1007/978-3-642-20505-7_26 10.1016/j.future.2006.10.008 10.1109/icsmc.2011.6083684 10.1109/cisda.2009.5356528 10.1109/icdar.1995.598994 10.1109/spdp.1994.346184 10.1109/isda.2009.181 10.7551/mitpress/3927.001.0001 |
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| Keywords | Parallel genetic algorithm Feature selection Machine learning Intrusion detection systems |
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5871_CR32 publication-title: IEEE Access doi: 10.1109/access.2019.2951750 – ident: 5871_CR3 – volume: 26 start-page: 997 issue: 3 year: 2013 ident: 5871_CR33 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2012.05.023 – ident: 5871_CR22 – ident: 5871_CR20 – volume: 82 start-page: 216 year: 2017 ident: 5871_CR4 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2017.04.017 – volume: 3 start-page: 262 issue: 4 year: 2000 ident: 5871_CR29 publication-title: ACM Trans Inf Syst Secur (TISSEC) doi: 10.1145/382912.382923 – volume: 10 start-page: 1 issue: 1 year: 2010 ident: 5871_CR27 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2009.06.019 – ident: 5871_CR5 doi: 10.7551/mitpress/3927.001.0001 – year: 2020 ident: 5871_CR38 publication-title: Adv Intell Syst Comput doi: 10.1007/978-981-32-9343-4_16 – volume: 30 start-page: 1601 issue: 5 year: 2018 ident: 5871_CR13 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2780-z – volume: 89 start-page: 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| SubjectTerms | Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Decision analysis Decision making Decision trees Feature selection Genetic algorithms Image Processing and Computer Vision Learning theory Machine learning Original Article Parallel processing Preprocessing Probability and Statistics in Computer Science Support vector machines |
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| Title | Feature selection using cloud-based parallel genetic algorithm for intrusion detection data classification |
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| Volume | 33 |
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