Machine Learning in Cybersecurity: Detecting and Preventing Phishing Attacks

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dc.contributor.advisorOláh, Norbert
dc.contributor.authorOta, Hina
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2025-06-26T21:40:48Z
dc.date.available2025-06-26T21:40:48Z
dc.date.created2025-03-24
dc.description.abstractThe thesis explores machine learning algorithms in cybersecurity, especially from the detection and prevention points of view against phishing attacks. Several classification models like Naive Bayes, Logistic Regression, SGD Classifier, Decision Tree, Random Forest, and XGBoost are trained and tested using a real-world email dataset. These machine learning models were compared using performance metrics such as accuracy and F1-score to analyze the strengths and weaknesses of each model. All the implementation details and performance results are on my Github repository.
dc.description.courseProgramtervező informatikus
dc.description.degreeBSc/BA
dc.format.extent28
dc.identifier.urihttps://hdl.handle.net/2437/394841
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectCybersecurity
dc.subjectMachine Learning
dc.subjectPhishing Attacks
dc.subject.dspaceInformatics::Computer Science
dc.titleMachine Learning in Cybersecurity: Detecting and Preventing Phishing Attacks
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