Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System
The integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attack...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9137
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9137
- Palabra clave:
- Classification model
Data set
Dos Attacks
Feature selection
Machine learning
Support vector machine
Artificial intelligence
Classification (of information)
Denial-of-service attack
Intrusion detection
Learning systems
Network security
Statistical tests
Support vector machines
Classification models
Cyber-attacks
Data set
Features selection
Intrusion Detection Systems
Support vector machine models
Feature extraction
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| title |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| spellingShingle |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System Classification model Data set Dos Attacks Feature selection Machine learning Support vector machine Artificial intelligence Classification (of information) Denial-of-service attack Intrusion detection Learning systems Network security Statistical tests Support vector machines Classification models Cyber-attacks Data set Features selection Intrusion Detection Systems Support vector machine models Feature extraction |
| title_short |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| title_full |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| title_fullStr |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| title_full_unstemmed |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| title_sort |
Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System |
| dc.contributor.editor.none.fl_str_mv |
Orjuela-Canon A.D. |
| dc.subject.keywords.none.fl_str_mv |
Classification model Data set Dos Attacks Feature selection Machine learning Support vector machine Artificial intelligence Classification (of information) Denial-of-service attack Intrusion detection Learning systems Network security Statistical tests Support vector machines Classification models Cyber-attacks Data set Features selection Intrusion Detection Systems Support vector machine models Feature extraction |
| topic |
Classification model Data set Dos Attacks Feature selection Machine learning Support vector machine Artificial intelligence Classification (of information) Denial-of-service attack Intrusion detection Learning systems Network security Statistical tests Support vector machines Classification models Cyber-attacks Data set Features selection Intrusion Detection Systems Support vector machine models Feature extraction |
| description |
The integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attacks like Denial of Service attacks. In this article, we will use all the attributes of the data-set NSL-KDD to train and test a Support Vector Machine model. This model will then be applied to a method of feature selection to obtain the most relevant attributes within the aforementioned data-set and train the model again. The main goal is comparing the results obtained in both instances of training and validate which was more efficient. © 2019 IEEE. |
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2019 |
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2019 |
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2020-03-26T16:33:02Z |
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2020-03-26T16:33:02Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Conferencia |
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2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings |
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9781728116143 |
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https://hdl.handle.net/20.500.12585/9137 |
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10.1109/ColCACI.2019.8781803 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57210565161 26325154200 |
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2019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings 9781728116143 10.1109/ColCACI.2019.8781803 Universidad Tecnológica de Bolívar Repositorio UTB 57210565161 26325154200 |
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https://hdl.handle.net/20.500.12585/9137 |
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eng |
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eng |
| dc.relation.conferencedate.none.fl_str_mv |
5 June 2019 through 7 June 2019 |
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http://purl.org/coar/access_right/c_16ec |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Recurso electrónico |
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Institute of Electrical and Electronics Engineers Inc. |
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Institute of Electrical and Electronics Engineers Inc. |
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Orjuela-Canon A.D.Álvarez Almeida L.A.Carlos Martinez Santos J.2020-03-26T16:33:02Z2020-03-26T16:33:02Z20192019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019 - Proceedings9781728116143https://hdl.handle.net/20.500.12585/913710.1109/ColCACI.2019.8781803Universidad Tecnológica de BolívarRepositorio UTB5721056516126325154200The integrity of information and services is one of the more evident concerns in the world of global information security, due to the fact that it has economic repercussions on the digital industry. For this reason, big companies spend a lot of money on systems that protect them against cyber-attacks like Denial of Service attacks. In this article, we will use all the attributes of the data-set NSL-KDD to train and test a Support Vector Machine model. This model will then be applied to a method of feature selection to obtain the most relevant attributes within the aforementioned data-set and train the model again. The main goal is comparing the results obtained in both instances of training and validate which was more efficient. © 2019 IEEE.EEE Colombia Section;EEE Colombian Caribbean Section;IEEE Computational Intelligence Colombian Chapter;IEEE Computational Intelligence SocietyRecurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85070855791&doi=10.1109%2fColCACI.2019.8781803&partnerID=40&md5=e5847944721efd67a906bd5aaabba5f9Scopus2-s2.0-850708557912019 IEEE Colombian Conference on Applications in Computational Intelligence, ColCACI 2019Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection Systeminfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fClassification modelData setDos AttacksFeature selectionMachine learningSupport vector machineArtificial intelligenceClassification (of information)Denial-of-service attackIntrusion detectionLearning systemsNetwork securityStatistical testsSupport vector machinesClassification modelsCyber-attacksData setFeatures selectionIntrusion Detection SystemsSupport vector machine modelsFeature extraction5 June 2019 through 7 June 2019(1999) Canadian Institute for Cybersecurity, , nsl-kdd DatasetDhanabal, L., Shantharajah, S.P., A study on nsl-kdd dataset for intrusion detection system based on classification algorithms (2015) International Journal of Advanced Research in Computer and Communication Engineering, 4 (6), pp. 446-452Fakieh, K., Survey on ddos attacks prevention and detection in cloud (2016) International Journal of Applied Information Systems, 12Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., The kdd process for extracting useful knowledge from volumes of data (1996) Communications of the ACM, 39 (11), pp. 27-34Gyanchandani, M., Rana, J.L., Yadav, R.N., Taxonomy of anomaly based intrusion detection system: A review (2012) International Journal of Scientific and Research Publications, 2 (12), pp. 1-13Kaur, P., Kumar, M., Bhandari, A., A review of detection approaches for distributed denial of service attacks (2017) Systems Science & Control Engineering, 5 (1), pp. 301-320. , JanuaryMarkou, M., Singh, S., Novelty detection: A reviewpart 2: Neural network based approaches (2003) Signal Processing, 83 (12), pp. 2499-2521Meti, N., Narayan, D.G., Baligar, V.P., Detection of distributed denial of service attacks using machine learning algorithms in software defined networks. In (2017) 2017 International Conference on Advances in Computing Communications and Informatics (ICACCI, pp. 1366-1371Parsaei, M.R., Rostami, S.M., Javidan, R., A hybrid data mining approach for intrusion detection on imbalanced nsl-kdd dataset (2016) International Journal of Advanced Computer Science and Applications, 7 (6), pp. 20-25Patcha, A., Park, J.-M., An overview of anomaly detection techniques: Existing solutions and latest technological trends (2007) Computer Networks, 51 (12), pp. 3448-3470Boddula, N., Kalime, S., A study on detection of distributed denial of service attacks using machine learning techniques (2018) International Journal of Research, p. 10Zargar, S.T., Joshi, J., Tipper, D., A survey of defense mechanisms against distributed denial of service (DDOS) flooding attacks (2013) IEEE Communications Surveys & Tutorials, 15 (4), pp. 2046-2069http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9137/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9137oai:repositorio.utb.edu.co:20.500.12585/91372021-02-02 14:55:27.323Repositorio Institucional UTBrepositorioutb@utb.edu.co |