Intelligent Anomaly Detection System for IoT
- Autores
- Bolatti, Diego; Karanik, Marcelo J.; Todt, Carolina; Scappini, Reinaldo José Ramón; Gramajo, Sergio D.
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The growing use of the Internet of Things (IoT) in different areas implies a proportional growth in threats and attacks on end devices. To solve this problem, the IoT systems must be equipped with an anomaly detection system (ADS). This work introduces the design of a hybrid ADS based on the Software-Defined Network (SDN) architecture, which combines the rule-based and Machine Learning-based detection technique. Whereas the rule-based approach is used to detect known attacks with the help of rules defined by security experts. And the Machine Learning approach is used to detect unknown attacks with the help of Artificial Intelligence techniques.
Facultad de Informática - Materia
-
Ciencias Informáticas
IoT
Anomaly detection
Machine learning - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
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- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125150
Ver los metadatos del registro completo
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Intelligent Anomaly Detection System for IoTBolatti, DiegoKaranik, Marcelo J.Todt, CarolinaScappini, Reinaldo José RamónGramajo, Sergio D.Ciencias InformáticasIoTAnomaly detectionMachine learningThe growing use of the Internet of Things (IoT) in different areas implies a proportional growth in threats and attacks on end devices. To solve this problem, the IoT systems must be equipped with an anomaly detection system (ADS). This work introduces the design of a hybrid ADS based on the Software-Defined Network (SDN) architecture, which combines the rule-based and Machine Learning-based detection technique. Whereas the rule-based approach is used to detect known attacks with the help of rules defined by security experts. And the Machine Learning approach is used to detect unknown attacks with the help of Artificial Intelligence techniques.Facultad de Informática2021info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf47-50http://sedici.unlp.edu.ar/handle/10915/125150enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4info:eu-repo/semantics/reference/hdl/10915/121564info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:22:01Zoai:sedici.unlp.edu.ar:10915/125150Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:22:01.329SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Intelligent Anomaly Detection System for IoT |
| title |
Intelligent Anomaly Detection System for IoT |
| spellingShingle |
Intelligent Anomaly Detection System for IoT Bolatti, Diego Ciencias Informáticas IoT Anomaly detection Machine learning |
| title_short |
Intelligent Anomaly Detection System for IoT |
| title_full |
Intelligent Anomaly Detection System for IoT |
| title_fullStr |
Intelligent Anomaly Detection System for IoT |
| title_full_unstemmed |
Intelligent Anomaly Detection System for IoT |
| title_sort |
Intelligent Anomaly Detection System for IoT |
| dc.creator.none.fl_str_mv |
Bolatti, Diego Karanik, Marcelo J. Todt, Carolina Scappini, Reinaldo José Ramón Gramajo, Sergio D. |
| author |
Bolatti, Diego |
| author_facet |
Bolatti, Diego Karanik, Marcelo J. Todt, Carolina Scappini, Reinaldo José Ramón Gramajo, Sergio D. |
| author_role |
author |
| author2 |
Karanik, Marcelo J. Todt, Carolina Scappini, Reinaldo José Ramón Gramajo, Sergio D. |
| author2_role |
author author author author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas IoT Anomaly detection Machine learning |
| topic |
Ciencias Informáticas IoT Anomaly detection Machine learning |
| dc.description.none.fl_txt_mv |
The growing use of the Internet of Things (IoT) in different areas implies a proportional growth in threats and attacks on end devices. To solve this problem, the IoT systems must be equipped with an anomaly detection system (ADS). This work introduces the design of a hybrid ADS based on the Software-Defined Network (SDN) architecture, which combines the rule-based and Machine Learning-based detection technique. Whereas the rule-based approach is used to detect known attacks with the help of rules defined by security experts. And the Machine Learning approach is used to detect unknown attacks with the help of Artificial Intelligence techniques. Facultad de Informática |
| description |
The growing use of the Internet of Things (IoT) in different areas implies a proportional growth in threats and attacks on end devices. To solve this problem, the IoT systems must be equipped with an anomaly detection system (ADS). This work introduces the design of a hybrid ADS based on the Software-Defined Network (SDN) architecture, which combines the rule-based and Machine Learning-based detection technique. Whereas the rule-based approach is used to detect known attacks with the help of rules defined by security experts. And the Machine Learning approach is used to detect unknown attacks with the help of Artificial Intelligence techniques. |
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2021 |
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2021 |
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