Comparison of ML Algorithms to Detect Vulnerabilities of RPL-Based IoT Devices in Intelligent and Fuzzy Systems

Basit Öğe Kaydı
dc.contributor.authorKiraz M.U.
dc.contributor.authorYilmaz A.
dc.date.accessioned2024-03-13T10:01:03Z
dc.date.available2024-03-13T10:01:03Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- -- 264409en_US
dc.description.abstractThe RPL protocol (Routing Protocol for Low-Power and Lossy Networks) was designed by IETF [1] for 6LoWPAN to optimize power consumption on the Internet of Things (IoT) devices. These devices have limited processing power, memory, and generally limited energy because they are battery-powered. RPL aims to establish the shortest distance by setting up n number of IoT devices through each other DAG (Directed Acyclic Graph) and therefore the most optimum energy consumption. However, due to the complex infrastructure of RPL and the low capacity of IoT devices, the RPL protocol operating at the network layer is susceptible to attacks. Therefore, it is vital to develop a fast, practical, uncomplicated, and reliable intrusion detection system in the network layer. In the event of an attack on IoT devices operating with the RPL protocol, an anomaly will occur in the network packets in the 3rd layer. Processing these packages with machine learning algorithms will make the detection of the attack extremely easy. In this article, “Decision Tree,” (DT) “Logistic Regression,” (LR) “Random Forest,” (RF) “Fuzzy Pattern Tree Classifier,” (FPTC), and “Neural Network” (NN) algorithms are compared for catching Flooding Attacks (FA), Version Number Increase Attacks (VNIA), and Decreased Rank (DRA) attacks. At the end of our study, it is observed that the Random forest algorithm gave better results than other algorithms in the system built by the study. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-85577-2_30
dc.identifier.endpage262en_US
dc.identifier.isbn9783030855765
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85115203414en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage254en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-85577-2_30
dc.identifier.urihttps://hdl.handle.net/20.500.12662/2944
dc.identifier.volume308en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecreased rank attacken_US
dc.subjectHello flood attacken_US
dc.subjectMachine learning algorithmsen_US
dc.subjectRPL attacksen_US
dc.subjectVersion number increase attacken_US
dc.titleComparison of ML Algorithms to Detect Vulnerabilities of RPL-Based IoT Devices in Intelligent and Fuzzy Systemsen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu