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dc.contributor.authorYavuz L.
dc.contributor.authorSoran A.
dc.contributor.authorOnen A.
dc.contributor.authorMuyeen S.M.
dc.date.accessioned2021-06-17T09:24:26Z
dc.date.available2021-06-17T09:24:26Z
dc.date.issued2020en_US
dc.identifier.isbn978-172816611-7
dc.identifier.urihttps://doi.org/10.1109/SPIES48661.2020.9243033
dc.identifier.urihttps://hdl.handle.net/20.500.12573/780
dc.description.abstractPower system protection units has got enormous importance with the growing risk of cyber-attacks. To create sustainable and well protected system, power system data must be healthy. For that purpose, many machine learning applications have been developed and used for bad data detection. However, each method has got different detection and application process. Methods has superiority over other methods. Although, an algorithm can detect some injections easily, same algorithm can be fail when injection type changed. So methods have got different success results when the injection types changed. For that reason, different injection types are applied on power system IEEE 14 bus system via created special hacking algorithm. PSCAD and python linkage has been used for simulation and detection parts. 3 different injection types created and applied on the system and five different most popular algorithms (SVM, k- NN, LDA, NB, LR) tested. Each algorithm's performances are compared and evaluated.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.isversionof10.1109/SPIES48661.2020.9243033en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSvmen_US
dc.subjectNben_US
dc.subjectLren_US
dc.subjectLdaen_US
dc.subjectKnnen_US
dc.subjectHacking algorithmen_US
dc.subjectBad data detectionen_US
dc.titleMachine learning algorithms against hacking attack and detection success comparisonen_US
dc.typeconferenceObjecten_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.identifier.volumePages 258 - 262en_US
dc.relation.journal2020 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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