Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems
Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world appli...
Ausführliche Beschreibung
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| Autor*in: |
Santos, Kelson Carvalho [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of network and systems management - Springer US, 1993, 32(2024), 2 vom: 22. März |
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| Übergeordnetes Werk: |
volume:32 ; year:2024 ; number:2 ; day:22 ; month:03 |
| Links: |
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| DOI / URN: |
10.1007/s10922-024-09813-z |
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| Katalog-ID: |
SPR055249892 |
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| 520 | |a Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. Specifically, we evaluated the effects of data cleaning, encoding, and normalization techniques on the performance of binary and multiclass intrusion detection models. This work investigates the impact of data preprocessing techniques on the performance of ML-based IDS and how the performance of different ML-based IDS is affected by data preprocessing techniques. To this end, we implemented a machine learning pipeline to apply the data preprocessing techniques in different scenarios to answer such questions. The findings analyzed using the Friedman statistical test and Nemenyi post-hoc test revealed significant differences in groups of data preprocessing techniques and ML-based IDS, according to the evaluation metrics. However, these differences were not observed in multiclass scenarios for data preprocessing techniques. Additionally, ML-based IDS exhibited varying performances in binary and multiclass classifications. Therefore, our investigation presents insights into the efficacy of different data preprocessing techniques for building robust and accurate intrusion detection models. | ||
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| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a ML-based IDS |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Miani, Rodrigo Sanches |4 aut | |
| 700 | 1 | |a de Oliveira Silva, Flávio |4 aut | |
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10.1007/s10922-024-09813-z doi (DE-627)SPR055249892 (SPR)s10922-024-09813-z-e DE-627 ger DE-627 rakwb eng 004 VZ 54.32 bkl 54.54 bkl Santos, Kelson Carvalho verfasserin aut Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. Specifically, we evaluated the effects of data cleaning, encoding, and normalization techniques on the performance of binary and multiclass intrusion detection models. This work investigates the impact of data preprocessing techniques on the performance of ML-based IDS and how the performance of different ML-based IDS is affected by data preprocessing techniques. To this end, we implemented a machine learning pipeline to apply the data preprocessing techniques in different scenarios to answer such questions. The findings analyzed using the Friedman statistical test and Nemenyi post-hoc test revealed significant differences in groups of data preprocessing techniques and ML-based IDS, according to the evaluation metrics. However, these differences were not observed in multiclass scenarios for data preprocessing techniques. Additionally, ML-based IDS exhibited varying performances in binary and multiclass classifications. Therefore, our investigation presents insights into the efficacy of different data preprocessing techniques for building robust and accurate intrusion detection models. Data preprocessing techniques (dpeaa)DE-He213 IDS (dpeaa)DE-He213 Intrusion detection system (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML-based IDS (dpeaa)DE-He213 Statistical test (dpeaa)DE-He213 Miani, Rodrigo Sanches aut de Oliveira Silva, Flávio aut Enthalten in Journal of network and systems management Springer US, 1993 32(2024), 2 vom: 22. März (DE-627)320575756 (DE-600)2017028-2 1573-7705 nnns volume:32 year:2024 number:2 day:22 month:03 https://dx.doi.org/10.1007/s10922-024-09813-z lizenzpflichtig Volltext SYSFLAG_0 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_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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 54.32 VZ 54.54 VZ AR 32 2024 2 22 03 |
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10.1007/s10922-024-09813-z doi (DE-627)SPR055249892 (SPR)s10922-024-09813-z-e DE-627 ger DE-627 rakwb eng 004 VZ 54.32 bkl 54.54 bkl Santos, Kelson Carvalho verfasserin aut Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. Specifically, we evaluated the effects of data cleaning, encoding, and normalization techniques on the performance of binary and multiclass intrusion detection models. This work investigates the impact of data preprocessing techniques on the performance of ML-based IDS and how the performance of different ML-based IDS is affected by data preprocessing techniques. To this end, we implemented a machine learning pipeline to apply the data preprocessing techniques in different scenarios to answer such questions. The findings analyzed using the Friedman statistical test and Nemenyi post-hoc test revealed significant differences in groups of data preprocessing techniques and ML-based IDS, according to the evaluation metrics. However, these differences were not observed in multiclass scenarios for data preprocessing techniques. Additionally, ML-based IDS exhibited varying performances in binary and multiclass classifications. Therefore, our investigation presents insights into the efficacy of different data preprocessing techniques for building robust and accurate intrusion detection models. Data preprocessing techniques (dpeaa)DE-He213 IDS (dpeaa)DE-He213 Intrusion detection system (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML-based IDS (dpeaa)DE-He213 Statistical test (dpeaa)DE-He213 Miani, Rodrigo Sanches aut de Oliveira Silva, Flávio aut Enthalten in Journal of network and systems management Springer US, 1993 32(2024), 2 vom: 22. März (DE-627)320575756 (DE-600)2017028-2 1573-7705 nnns volume:32 year:2024 number:2 day:22 month:03 https://dx.doi.org/10.1007/s10922-024-09813-z lizenzpflichtig Volltext SYSFLAG_0 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_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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 54.32 VZ 54.54 VZ AR 32 2024 2 22 03 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. 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Santos, Kelson Carvalho |
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004 VZ 54.32 bkl 54.54 bkl Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems Data preprocessing techniques (dpeaa)DE-He213 IDS (dpeaa)DE-He213 Intrusion detection system (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 ML-based IDS (dpeaa)DE-He213 Statistical test (dpeaa)DE-He213 |
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evaluating the impact of data preprocessing techniques on the performance of intrusion detection systems |
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Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems |
| abstract |
Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. Specifically, we evaluated the effects of data cleaning, encoding, and normalization techniques on the performance of binary and multiclass intrusion detection models. This work investigates the impact of data preprocessing techniques on the performance of ML-based IDS and how the performance of different ML-based IDS is affected by data preprocessing techniques. To this end, we implemented a machine learning pipeline to apply the data preprocessing techniques in different scenarios to answer such questions. The findings analyzed using the Friedman statistical test and Nemenyi post-hoc test revealed significant differences in groups of data preprocessing techniques and ML-based IDS, according to the evaluation metrics. However, these differences were not observed in multiclass scenarios for data preprocessing techniques. Additionally, ML-based IDS exhibited varying performances in binary and multiclass classifications. Therefore, our investigation presents insights into the efficacy of different data preprocessing techniques for building robust and accurate intrusion detection models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. Specifically, we evaluated the effects of data cleaning, encoding, and normalization techniques on the performance of binary and multiclass intrusion detection models. This work investigates the impact of data preprocessing techniques on the performance of ML-based IDS and how the performance of different ML-based IDS is affected by data preprocessing techniques. To this end, we implemented a machine learning pipeline to apply the data preprocessing techniques in different scenarios to answer such questions. The findings analyzed using the Friedman statistical test and Nemenyi post-hoc test revealed significant differences in groups of data preprocessing techniques and ML-based IDS, according to the evaluation metrics. However, these differences were not observed in multiclass scenarios for data preprocessing techniques. Additionally, ML-based IDS exhibited varying performances in binary and multiclass classifications. Therefore, our investigation presents insights into the efficacy of different data preprocessing techniques for building robust and accurate intrusion detection models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. Specifically, we evaluated the effects of data cleaning, encoding, and normalization techniques on the performance of binary and multiclass intrusion detection models. This work investigates the impact of data preprocessing techniques on the performance of ML-based IDS and how the performance of different ML-based IDS is affected by data preprocessing techniques. To this end, we implemented a machine learning pipeline to apply the data preprocessing techniques in different scenarios to answer such questions. The findings analyzed using the Friedman statistical test and Nemenyi post-hoc test revealed significant differences in groups of data preprocessing techniques and ML-based IDS, according to the evaluation metrics. However, these differences were not observed in multiclass scenarios for data preprocessing techniques. Additionally, ML-based IDS exhibited varying performances in binary and multiclass classifications. Therefore, our investigation presents insights into the efficacy of different data preprocessing techniques for building robust and accurate intrusion detection models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Evaluating the Impact of Data Preprocessing Techniques on the Performance of Intrusion Detection Systems |
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Miani, Rodrigo Sanches de Oliveira Silva, Flávio |
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| score |
7.4000826 |