Keywords : Attackers
Detection and Identification of Bogus Profiles in online Social Network using Machine Learning Methods
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 4, Pages 395-400
Here current creation online social networks (OSNs) become more and more common and the social life of people has become more linked to these pages. They use OSNs to remain in finger with everyone else, distribute news, prepare dealings and still run their personal e-. Out of control of the OSN's evolution and the huge extent of their supporters 'individual developments, they have been attackers and impostors who take individual information, share fake news and disseminate vindictive exercises. Researchers in various fields began inspecting environmentally friendly techniques in order to perform abnormal activity and counterfeit money that is based on accounting and classification algorithms . However, the use of stand-alone classification algorithms no longer yields a straightforward outcome, some of the factors that are manipulated by the account have a low influence or have no impact in the closing results. The paper proposes to use the SVM-NN as a modern algorithm to effectively identify suspected Twitter accounts and bots, to add four choices and to restrict measurements. Three laptop classification mastering algorithms were used to determine the actual or false identity of target accounts. They included the SVM, the Neural Network and our recently urbanized SVM-NN method that utilizes far less hardware but is still able to correctly identify about 98% of the money due to the training data set.