Neural networks as tool to improve the intrusion detection system
Nowadays, computer programs affecting computers both locally and network-wide have led to the design and development of different preventive and corrective strategies to remedy computer security problems. This dynamic has been important for the understanding of the structure of attacks and how best...
- Autores:
-
Esmeral, Ernesto
Mardini, Johan
Salcedo, Dixon
De-La-Hoz-Franco, Emiro
Avendaño, Inirida
Henriquez, Carlos
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8828
- Acceso en línea:
- https://hdl.handle.net/11323/8828
https://repositorio.cuc.edu.co/
- Palabra clave:
- GHSOM neural networks
IDS
NSL_KDD
SOM neural networks
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Neural networks as tool to improve the intrusion detection system |
| title |
Neural networks as tool to improve the intrusion detection system |
| spellingShingle |
Neural networks as tool to improve the intrusion detection system GHSOM neural networks IDS NSL_KDD SOM neural networks |
| title_short |
Neural networks as tool to improve the intrusion detection system |
| title_full |
Neural networks as tool to improve the intrusion detection system |
| title_fullStr |
Neural networks as tool to improve the intrusion detection system |
| title_full_unstemmed |
Neural networks as tool to improve the intrusion detection system |
| title_sort |
Neural networks as tool to improve the intrusion detection system |
| dc.creator.fl_str_mv |
Esmeral, Ernesto Mardini, Johan Salcedo, Dixon De-La-Hoz-Franco, Emiro Avendaño, Inirida Henriquez, Carlos |
| dc.contributor.author.spa.fl_str_mv |
Esmeral, Ernesto Mardini, Johan Salcedo, Dixon De-La-Hoz-Franco, Emiro Avendaño, Inirida Henriquez, Carlos |
| dc.subject.spa.fl_str_mv |
GHSOM neural networks IDS NSL_KDD SOM neural networks |
| topic |
GHSOM neural networks IDS NSL_KDD SOM neural networks |
| description |
Nowadays, computer programs affecting computers both locally and network-wide have led to the design and development of different preventive and corrective strategies to remedy computer security problems. This dynamic has been important for the understanding of the structure of attacks and how best to counteract them, making sure that their impact is less than expected by the attacker. For this research, a simulation was carried out using the DATASET-KDD NSL at 100%, generating an experimental environment, where processes of pre-processing, training, classification, and evaluation of model quality metrics were carried out. Likewise, a comparative analysis of the results obtained after implementing different feature selection techniques (INFO.GAIN, GAIN RATIO, and ONE R), and classification techniques based on neural networks that use an unsupervised learning algorithm based on self-organizing maps (SOM and GHSOM), with the purpose of classifying bi-class network traffic automatically. From the above, a 97.09% hit rate was obtained with 21 features by implementing the GHSOM classifier with 10-fold cross-validation with the ONE R feature selection technique, which would improve the efficiency and performance of Intrusion Detection Systems (IDS). |
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2021 |
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2021-11-02T20:11:09Z |
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2021-11-02T20:11:09Z |
| dc.date.issued.none.fl_str_mv |
2021-09-17 |
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Capítulo - Parte de Libro |
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http://purl.org/coar/resource_type/c_3248 |
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Text |
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info:eu-repo/semantics/bookPart |
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http://purl.org/redcol/resource_type/CAP_LIB |
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acceptedVersion |
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03029743 |
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https://hdl.handle.net/11323/8828 |
| dc.identifier.doi.spa.fl_str_mv |
10.1007/978-3-030-84340-3_10 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Kimani, K., Oduol, V., Langat, K.: Cyber security challenges for IoT-based smart grid networks. Int. J. Crit. Infrastruct. Protect. 25, 36–49 (2019) Guerrero, C.D., Salcedo, D., Lamos, H.: A clustering approach to reduce the available bandwidth estimation error. IEEE Lat. Am. Trans. 11(3), 927–932 (2013) Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.): Advanced Computing, Networking and Informatics- Volume 1. SIST, vol. 27. Springer, Cham (2014). Ibrahim, H.E., Badr, S.M., Shaheen, M.A.: Adaptive layered approach using machine learning techniques with gain ratio for intrusion detection systems. arXiv preprint arXiv:1210.7650 (2012) Barletta, V.S., Caivano, D., Nannavecchia, A., Scalera, M.: Intrusion detection for in-vehicle communication networks: an unsupervised Kohonen SOM approach. Future Internet 12(7), 119 (2020) Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993) Kohonen, T.: Analysis of a simple self-organizing process. Biol. Cybern. 44(2), 135–140 (1982) Dittenbach, M., Merkl, D., Rauber, A.: Organizing and exploring high-dimensional data with the growing hierarchical self-organizing map. In: FSKD, pp. 626–630 (2002) Sánchez-maroño, V.B.N.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34, 483–519 (2013). Spolâ, N., Monard, M.C.: Label construction for multi-label feature selection (2014). Kaur, R., Kumar, G., Kumar, K.: A comparative study of feature selection techniques for intrusion detection. In: 2nd International Conference on Computing for Sustainable Global Development (2015) Singh, R., Kumar, H., Singla, R.K.: Analysis of feature selection techniques for network traffic dataset. In: 2013 International Conference on Machine Intelligence and Research Advancement (ICMIRA), pp. 42–46. IEEE (2013) Ghosh, M., Guha, R., Sarkar, R., Abraham, A.: A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput. Appl. 32, 7839–7857 (2019) Ali, M.: An ensemble-based feature selection methodology for case-based learning. Doctoral dissertation, University of Tasmania (2018) Osanaiye, O., Cai, H., Choo, K.-K., Dehghantanha, A., Xu, Z., Dlodlo, M.: Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP J. Wirel. Commun. Netw. 2016(1), 1 (2016). Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013). Enache, A.-C., Sgarciu, V.: Anomaly intrusions detection based on support vector machines with bat algorithm. In: 2014 18th International Conference on System Theory, Control and Computing (ICSTCC), pp. 856–861 (2014). Ferles, C., Papanikolaou, Y., Naidoo, K.J.: Denoising autoencoder self-organizing map (DASOM). Neural Netw. 105, 112–131 (2018) Dai, J., Xu, Q.: Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl. Soft Comput. 13(1), 211–221 (2013) Aranda, Y.R., Sotolongo, A.R.: Integración de los algoritmos de minería de datos 1R, PRISM e ID3 a PostgreSQL. JISTEM-J. Inf. Syst. Technol. Manage. 10(2), 389–406 (2013) Chen, A.M., Lu, H.M., Hecht-Nielsen, R.: Sobre la geometría de las superficies de error de red neuronal de avance. Cálculo Neuronal 5(6), 910–927 (1993) Chiu, C. H., Chen, J.J., Yu, F.: An effective distributed ghsom algorithm for unsupervised clustering on big data. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 297–304 (2017) Kohonen, T.: Associative Memory: A System-Theoretical Approach, vol. 17. Springer, Heidelberg (2012) Kohonen, T.: Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013) De La Hoz Franco, E., Ortiz Garcia, A., Ortega Lopera, J., De La Hoz Correa, E., Mendoza Palechor, F.: Implementation of an intrusion detection system based on self organizing map. J. Theor. Appl. Inf. Technol. 71(3), 324–334 (2015) Rauber, A., Merkl, D., Dittenbach, M.: The GHSOM Architecture and Training Process. Department of Software Technology, Vienna University of Technology (2016) Dittenbach, M., Merkl, D., Rauber, A.: The growing hierarchical self-organizing map. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, vol. 6, pp. 15–19. IEEE (2000) |
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Esmeral, ErnestoMardini, JohanSalcedo, DixonDe-La-Hoz-Franco, EmiroAvendaño, IniridaHenriquez, Carlos2021-11-02T20:11:09Z2021-11-02T20:11:09Z2021-09-1703029743https://hdl.handle.net/11323/882810.1007/978-3-030-84340-3_10Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Nowadays, computer programs affecting computers both locally and network-wide have led to the design and development of different preventive and corrective strategies to remedy computer security problems. This dynamic has been important for the understanding of the structure of attacks and how best to counteract them, making sure that their impact is less than expected by the attacker. For this research, a simulation was carried out using the DATASET-KDD NSL at 100%, generating an experimental environment, where processes of pre-processing, training, classification, and evaluation of model quality metrics were carried out. Likewise, a comparative analysis of the results obtained after implementing different feature selection techniques (INFO.GAIN, GAIN RATIO, and ONE R), and classification techniques based on neural networks that use an unsupervised learning algorithm based on self-organizing maps (SOM and GHSOM), with the purpose of classifying bi-class network traffic automatically. From the above, a 97.09% hit rate was obtained with 21 features by implementing the GHSOM classifier with 10-fold cross-validation with the ONE R feature selection technique, which would improve the efficiency and performance of Intrusion Detection Systems (IDS).Esmeral, Ernesto-will be generated-orcid-0000-0002-7526-8349-600Mardini, JohanSalcedo, Dixon-will be generated-orcid-0000-0002-3762-8462-600De-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Avendaño, IniridaHenriquez, Carlosapplication/pdfengInternational Conference on Computer Information Systems and Industrial Management CISIM 2021: Computer Information Systems and Industrial ManagementCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2GHSOM neural networksIDSNSL_KDDSOM neural networksNeural networks as tool to improve the intrusion detection systemCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/acceptedVersionhttps://link.springer.com/chapter/10.1007/978-3-030-84340-3_10Kimani, K., Oduol, V., Langat, K.: Cyber security challenges for IoT-based smart grid networks. Int. J. Crit. Infrastruct. Protect. 25, 36–49 (2019)Guerrero, C.D., Salcedo, D., Lamos, H.: A clustering approach to reduce the available bandwidth estimation error. IEEE Lat. Am. Trans. 11(3), 927–932 (2013)Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.): Advanced Computing, Networking and Informatics- Volume 1. SIST, vol. 27. Springer, Cham (2014).Ibrahim, H.E., Badr, S.M., Shaheen, M.A.: Adaptive layered approach using machine learning techniques with gain ratio for intrusion detection systems. arXiv preprint arXiv:1210.7650 (2012)Barletta, V.S., Caivano, D., Nannavecchia, A., Scalera, M.: Intrusion detection for in-vehicle communication networks: an unsupervised Kohonen SOM approach. Future Internet 12(7), 119 (2020)Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)Kohonen, T.: Analysis of a simple self-organizing process. Biol. Cybern. 44(2), 135–140 (1982)Dittenbach, M., Merkl, D., Rauber, A.: Organizing and exploring high-dimensional data with the growing hierarchical self-organizing map. In: FSKD, pp. 626–630 (2002)Sánchez-maroño, V.B.N.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34, 483–519 (2013).Spolâ, N., Monard, M.C.: Label construction for multi-label feature selection (2014).Kaur, R., Kumar, G., Kumar, K.: A comparative study of feature selection techniques for intrusion detection. In: 2nd International Conference on Computing for Sustainable Global Development (2015)Singh, R., Kumar, H., Singla, R.K.: Analysis of feature selection techniques for network traffic dataset. In: 2013 International Conference on Machine Intelligence and Research Advancement (ICMIRA), pp. 42–46. IEEE (2013)Ghosh, M., Guha, R., Sarkar, R., Abraham, A.: A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput. Appl. 32, 7839–7857 (2019)Ali, M.: An ensemble-based feature selection methodology for case-based learning. Doctoral dissertation, University of Tasmania (2018)Osanaiye, O., Cai, H., Choo, K.-K., Dehghantanha, A., Xu, Z., Dlodlo, M.: Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP J. Wirel. Commun. Netw. 2016(1), 1 (2016).Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013).Enache, A.-C., Sgarciu, V.: Anomaly intrusions detection based on support vector machines with bat algorithm. In: 2014 18th International Conference on System Theory, Control and Computing (ICSTCC), pp. 856–861 (2014).Ferles, C., Papanikolaou, Y., Naidoo, K.J.: Denoising autoencoder self-organizing map (DASOM). Neural Netw. 105, 112–131 (2018)Dai, J., Xu, Q.: Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl. Soft Comput. 13(1), 211–221 (2013)Aranda, Y.R., Sotolongo, A.R.: Integración de los algoritmos de minería de datos 1R, PRISM e ID3 a PostgreSQL. JISTEM-J. Inf. Syst. Technol. Manage. 10(2), 389–406 (2013)Chen, A.M., Lu, H.M., Hecht-Nielsen, R.: Sobre la geometría de las superficies de error de red neuronal de avance. Cálculo Neuronal 5(6), 910–927 (1993)Chiu, C. H., Chen, J.J., Yu, F.: An effective distributed ghsom algorithm for unsupervised clustering on big data. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 297–304 (2017)Kohonen, T.: Associative Memory: A System-Theoretical Approach, vol. 17. Springer, Heidelberg (2012)Kohonen, T.: Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013)De La Hoz Franco, E., Ortiz Garcia, A., Ortega Lopera, J., De La Hoz Correa, E., Mendoza Palechor, F.: Implementation of an intrusion detection system based on self organizing map. J. Theor. Appl. Inf. Technol. 71(3), 324–334 (2015)Rauber, A., Merkl, D., Dittenbach, M.: The GHSOM Architecture and Training Process. Department of Software Technology, Vienna University of Technology (2016)Dittenbach, M., Merkl, D., Rauber, A.: The growing hierarchical self-organizing map. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, vol. 6, pp. 15–19. IEEE (2000)PublicationORIGINALNeural Networks as Tool to Improve the Intrusion Detection System.pdfNeural Networks as Tool to Improve the Intrusion Detection System.pdfapplication/pdf408408https://repositorio.cuc.edu.co/bitstreams/20e5a049-d8ba-4927-8f45-b685eea2ebd3/download6a5dbbd5550546f5543921a4f4b3079eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/a8ef98ea-99ac-4a45-b451-be301833ebf4/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/e627d6f0-975b-4a97-8ff3-7dcf09d29f74/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILNeural Networks as Tool to Improve the Intrusion Detection System.pdf.jpgNeural Networks as Tool to Improve the Intrusion Detection System.pdf.jpgimage/jpeg44537https://repositorio.cuc.edu.co/bitstreams/a3591ad1-789d-4d8c-85c3-a733bc9ea31a/downloada84a1875dae9b9feffd9ee9a46f2c5dfMD54TEXTNeural Networks as 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