Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/472934
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dc.coverage.spatialMachine learning approach for mitigating security threats in iot environment
dc.date.accessioned2023-03-28T07:07:13Z-
dc.date.available2023-03-28T07:07:13Z-
dc.identifier.urihttp://hdl.handle.net/10603/472934-
dc.description.abstractVarious entities of the human community like healthcare, smart newlinehome, smart grid etc., integrated with the internet, and thus iot emerged. in newlinethis modern era, vast amount of facts and information has been transferred via newlinewireless networks. such networks give rise to innumerable cyber threats and newlineprivacy issues. the aberrant activities which are pernicious to the network are newlinetermed intrusions. the iot networks are pervasive and thus opens to many newlinesecurity threats. if the iot framework suffers from cyber threats, it leads to newlineinformation loss or data loss and sluggishness in iot devices. intrusion newlinedetection system has been utilized for the past two decagons to secure the newlineinformation and networks. as iot has distinct standards and protocol stacks, newlinethe intrusions in iot can not be recognized by classical intrusion detection newlineapproaches. as iot generates boundless data, it is strenuous to scrutinize newlinethem frequently. an intrusion detection system acts as the surveillance to newlinedefend the system or network from attacks.machine learning approaches are excellent and influential techniques to resolve these unusual threats. a potent machine learning algorithm is deployed to obtain attack-free networks. aiming to solve thesecurity issues in the network layer of iot system, we propose a novel intrusion detection scheme that consists of three phases, namely,1. optimal feature vector selection (ofvs)2. bi-layer intrusion detection system (blid) newline3. distributed training in fog node (dt-fn)various entities of the human community like healthcare, smart home, smart grid etc., integrated with the internet, and thus iot emerged. in this modern era, vast amount of facts and information has been transferred via wireless networks. such networks give rise to innumerable cyber threats and privacy issues. the aberrant activities which are pernicious to the network are termed intrusions. the iot networks are pervasive and thus opens to many security threats. if the iot framework suffers from cyber threats, it leads to newlineinformation loss or data loss
dc.format.extentxxxi,219p.
dc.languageEnglish
dc.relationp.208-218
dc.rightsuniversity
dc.titleMachine learning approach for mitigating security threats in iot environment
dc.title.alternative
dc.creator.researcherShinly Swarna Sugi, S
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordNetwork security
dc.subject.keywordDistributed training
dc.subject.keywordIntrusion detection system
dc.description.note
dc.contributor.guideRaja Ratna, S
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File98.17 kBAdobe PDFView/Open
02_prelim pages.pdf2.08 MBAdobe PDFView/Open
03_content.pdf87.18 kBAdobe PDFView/Open
04_abstract.pdf114.31 kBAdobe PDFView/Open
05_chapter 1.pdf849.32 kBAdobe PDFView/Open
06_chapter 2.pdf190.6 kBAdobe PDFView/Open
07_chapter 3.pdf2.29 MBAdobe PDFView/Open
08_chapter 4.pdf1.68 MBAdobe PDFView/Open
09_chapter 5.pdf2.96 MBAdobe PDFView/Open
10_chapter 6.pdf85.31 kBAdobe PDFView/Open
11_annexures.pdf2.23 MBAdobe PDFView/Open
80_recommendation.pdf158.39 kBAdobe PDFView/Open


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