Classification of Fake Profiles Features in Facebook using K-Nearest Neighbour (KNN), Neural Network (NN) and Support Vector Machine (SVM)
Today, people rely heavily on Online Social Networks (OSN) which have piqued the interest of cyber criminals to carry out malicious acts. Coupled with the existence of illegal companies that make transactions with fake account services. To cater this fake account problem in OSN, this study focuses o...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://ir.upnm.edu.my/id/eprint/250/1/CLASSIFICATION%20OF%20FAKE%20%2825p%29.pdf http://ir.upnm.edu.my/id/eprint/250/2/CLASSIFICATION%20OF%20FAKE%20%28Full%29.pdf |
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| Summary: | Today, people rely heavily on Online Social Networks (OSN) which have piqued the interest of cyber criminals to carry out malicious acts. Coupled with the existence of illegal companies that make transactions with fake account services. To cater this fake account problem in OSN, this study focuses on identifying the most widely used fake Facebook accounts in OSN. It is included for their behaviour and features of fake account. This research methodology begins with data collection, identification of training and classifier functions, and finally validation and verification. The first process is to collect information about real and fake Facebook accounts. The second process is to use Facebook user feed data to understand user profile activity and identify the full set of features that play an important role in differentiating fake users from real Facebook users. Finally, we use these functions and identify the most important classifiers based on machine learning, which is mapping the identification of a total of 3 classifiers, namely K-nearest neighbour (KNN), support vector machine (SVM), and neural network (NN). The findings have revealed that the series of result shown that prediction the fake profile with good value of Classifying Accuracy (CA) which is KNN is 92%, NN is 94% and SVM is 95%. Same goes to Area Under Curve (AUC) which is KNN is 97%, NN is 98% and SVM is 98%. Ultimately, this finding will provide a new endeavour for countermeasure and protection of OSN users. |
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