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dc.date.accessioned 2021-09-20T12:34:57Z
dc.date.available 2021-09-20T12:34:57Z
dc.date.issued 2021
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/125150
dc.description.abstract 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. en
dc.format.extent 47-50 es
dc.language en es
dc.subject IoT es
dc.subject Anomaly detection es
dc.subject Machine learning es
dc.title Intelligent Anomaly Detection System for IoT en
dc.type Objeto de conferencia es
sedici.identifier.isbn 978-950-34-2016-4 es
sedici.creator.person Bolatti, Diego es
sedici.creator.person Karanik, Marcelo J. es
sedici.creator.person Todt, Carolina es
sedici.creator.person Scappini, Reinaldo José Ramón es
sedici.creator.person Gramajo, Sergio D. es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.date.exposure 2021
sedici.relation.event IX Jornadas de Cloud Computing, Big Data & Emerging Topics (Modalidad virtual, 22 al 25 de junio de 2021) es
sedici.description.peerReview peer-review es
sedici.relation.isRelatedWith http://sedici.unlp.edu.ar/handle/10915/121564 es
sedici.relation.bookTitle Short papers of the 9th Conference on Cloud Computing Conference, Big Data & Emerging Topics es


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)