Analysing Facebook user risk using machine learning algorithm

Abstract

Now-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.