Deep learning and machine learning based anomaly detection in internet of things environments

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dc.authoridÇalhan, Ali/0000-0002-5798-3103
dc.authorwosidÇalhan, Ali/H-1375-2014
dc.contributor.authorGökdemir, Ali
dc.contributor.authorÇalhan, Ali
dc.date.accessioned2023-07-26T11:58:01Z
dc.date.available2023-07-26T11:58:01Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractGraphical/Tabular Abstract Classical machine learning and deep learning were compared in detecting attacks on IoT environments. Due to its success in anomaly detection in the literature, Support Vector Machines (SVM) and Naive Bayes (NB) algorithms from classical machine learning algorithms were preferred. As a deep learning algorithm, the Long Short-Term Memory (LSTM) algorithm, which is mostly used in fields such as natural language processing and text processing, and which has very few studies in anomaly detection, has been chosen. With the LSTM algorithm, higher values were obtained in accuracy and f1 scores. Figure A. Proposed system model for anomaly detection in IoT environments with LSTM-SVM-NB algorithms Purpose: As the use of Internet of Things (IoT) systems has become widespread, cyber-attacks against these systems have also increased. Cyber-attacks occurring in IoT environments can include different types of attacks, such as the inability of their devices to serve, corruption, data capture, modification, or deletion. In this study, it is tried to predict duplication, interception, and modification attacks in Message Queuing Telemetry Transport (MQTT) messages using an IoT dataset with artificial intelligence techniques. Theory and Methods: In this study, compared to the performance metrics of SVM and NB, which are machine learning algorithms, and LSTM, which is a deep learning algorithm. Results: Experimental results show that the LSTM algorithm can be used in anomaly detection in the cyber security area, apart from natural language processing and text processing, which are the areas widely used in the literature. Besides, it was concluded that the LSTM algorithm achieved higher accuracy than the classical machine learning algorithms. Conclusion: In this paper, a comparison of deep learning and machine learning algorithms for anomaly detection in IoT environments is made. The results show that the LSTM algorithm, gives more effective results in anomaly detection than classical machine learning algorithms, but has some disadvantages in terms of working time.en_US
dc.identifier.doi10.17341/gazimmfd.962375
dc.identifier.endpage1956en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85128770373en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1945en_US
dc.identifier.trdizinid508765en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.962375
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/508765
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13378
dc.identifier.volume37en_US
dc.identifier.wosWOS:000767316300017en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorÇalhan, Ali
dc.language.isoenen_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectIot; Machine Learning; Deep Learning; Anomaly; Iot Securityen_US
dc.subjectAttacks; Schemeen_US
dc.titleDeep learning and machine learning based anomaly detection in internet of things environmentsen_US
dc.title.alternativeNesnelerin interneti ortamlarinda derin ö?renme ve makine ö?renmesi tabanli anomali tespitien_US
dc.typeArticleen_US

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