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dc.contributor.advisorArif, Hossain
dc.contributor.advisorIslam, Md. Saiful
dc.contributor.authorBarua, Arnab
dc.contributor.authorAdnan, Fahim
dc.contributor.authorGhosh, Ananna
dc.date.accessioned2021-05-29T16:05:45Z
dc.date.available2021-05-29T16:05:45Z
dc.date.copyright2020
dc.date.issued2020-04
dc.identifier.otherID: 15301012
dc.identifier.otherID: 15101023
dc.identifier.otherID: 19141020
dc.identifier.urihttp://dspace.bracu.ac.bd/xmlui/handle/10361/14447
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 40-42).
dc.description.abstractNow-a-days people exchange their personal information and interact with companions and close relatives in a way which is revolutionized. In any case, the majority of them don’t have the foggiest idea how to utilize, where to click, where not to, where to remark, and where not to. A considerable lot of them are posting in Facebook anything they desire and wish. This posting, fellowship and so on once in a while brings shocking occasions like identity theft, phishing, Cyber-wrongdoing and so on. So, Social media security has captured a great concern among the public and authority. At present, many features have been added to reduce the risk of hacking information. It is widely acknowledged that these features have played an important role in the security system. The essential focus point of our paper is on the safety implications of consumers posting their own Facebook information. We have made a survey containing 44 inquiries dependent on Facebook clients’ propensity and different things. We have looked at the ongoing information security rupture on Facebook through certain data mining substances. We have targeted three questions about victim of malware, identity theft, and phishing. From, our dataset we will know how many were victim of the three target parameter. We have implemented machine learning algorithms like ANN, XGBoost, SVM, Random Forest, Decision Tree, Gaussian Naive Bayes, Logistic Regression to identify the percentage of how many Facebook accounts are in risk and safe. Moreover, we will compare the best possible approach and worst approach among the algorithms to find the result. Among the models, we see ANN providing us the best result for the three labels with 89.89%, 94.94% and 86.86%. This research illustrates how different machine learning algorithms predicts the risk of Facebook users and which algorithm is most and least suitable to use in this scenario.en_US
dc.description.statementofresponsibilityArnab Barua
dc.description.statementofresponsibilityFahim Adnan
dc.description.statementofresponsibilityAnanna Ghosh
dc.format.extent42 pages
dc.language.isoen_USen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectIdentity theften_US
dc.subjectPhishingen_US
dc.subjectMalwareen_US
dc.subjectData Miningen_US
dc.subjectMachine Learningen_US
dc.subjectAlgorithmsen_US
dc.subjectANNen_US
dc.subjectSVMen_US
dc.subjectXGBoosten_US
dc.titleAnalysing Facebook user risk using machine learning algorithmen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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