Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/380943
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dc.date.accessioned2022-05-18T07:14:21Z-
dc.date.available2022-05-18T07:14:21Z-
dc.identifier.urihttp://hdl.handle.net/10603/380943-
dc.description.abstractPhishing detection is one of the important and demanding tasks in recent newlinedays for avoiding the illegal activities and information theft. Typically, the newlinephishing is a kind of squeezing, where the attacker is termed as phisher who newlineintends to misuse the authorized confidential data of the web users. The general newlineinformation whipped by the phishing attacker are the user name and password newlinedetails of the individual user, account number, e-banking information, and credit newlinecard details. So, it is highly important to prevent the user information and system newlineagainst the harmful phishing attacks. For this purpose, different types of newlinemechanisms have been developed in the conventional works to detect the newlinephishing attacks. The major drawbacks of the existing techniques are high time newlinecomplexity, and reduced accuracy.In order to solve these problems, enhanced security techniques are newlinedeveloped in the proposed work, which helps to detect the website phishing newlineattacks with increased detection accuracy. An enhanced Bidirectional long short- newlineterm memory (BiLSTM) Repetitive Neural Network (RNN) is developed in the newlinefirst module of this work for identifying the web phishing attacks. Then, an newlineeXtreme Gradient Boost (XGB) classification technique is utilized for detecting newlinethe phishing attacks based on the set of multiple features.Moreover, the Dynamic Threshold Algorithm (DTA) is deployed for newlinecategorizing the types of phishing attacks by incorporating the hybrid and visual newlinesimilarity features. Moreover, the performance of these techniques are validated newlineand compared by using different evaluation metrics. The results are compared with the recent state-of-the-art methods for proving the betterment of the proposed technique.
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dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleAn Effective Phishing Detection Model using Hybrid Features Algorithm and Deep Learning Techniques
dc.title.alternative
dc.creator.researcherArivukarasi, M
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.notePhishing, Deep Learning, Recurrent neural network, Bidirectional long short-term memory.
dc.contributor.guideAntoni Doss, A
dc.publisher.placeChennai
dc.publisher.universityHindustan University
dc.publisher.institutionDepartment of Computer Science and Engineering
dc.date.registered2018
dc.date.completed2022
dc.date.awarded
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science and Engineering

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01. title.pdfAttached File62.82 kBAdobe PDFView/Open
02. proceeding&bonafide certificate.pdf989.52 kBAdobe PDFView/Open
03. declaration.pdf61.62 kBAdobe PDFView/Open
04. acknowledgement.pdf27.68 kBAdobe PDFView/Open
05. contents.pdf30.37 kBAdobe PDFView/Open
06. abstract.pdf26.65 kBAdobe PDFView/Open
07. tables.pdf23.23 kBAdobe PDFView/Open
08. figures.pdf49.47 kBAdobe PDFView/Open
09. chapter 1.pdf1.29 MBAdobe PDFView/Open
10. chapter 2.pdf1.34 MBAdobe PDFView/Open
11. chapter 3.pdf1.65 MBAdobe PDFView/Open
12. chapter 4.pdf3.02 MBAdobe PDFView/Open
13. chapter 5.pdf31.48 kBAdobe PDFView/Open
14. chapter 6.pdf29.96 kBAdobe PDFView/Open
15. chapter 7.pdf25.89 kBAdobe PDFView/Open
16. reference.pdf85.4 kBAdobe PDFView/Open
17.publications.pdf2.01 MBAdobe PDFView/Open
80_recommendation.pdf140.18 kBAdobe PDFView/Open


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