Detecting Fraudulent Marketing in Online Social Networks and Mitigating Cyber Threats with Advanced Security Approaches

Using their fake marketing postings on social networking platforms, the unscrupulous people attempt to grab the attention of the customers. When it comes to Social Networking Services (SNS), it might be difficult to spot fraudulent postings that are posted by fictional individuals. It is for this reason that there is a need to identify fraudulent postings on social networking sites. Specifically, a technique known as SNS Fraudulent Detection (SFD) is being suggested in order to identify these harmful marketing postings. The DFA-T and WC based WC-NFS are both components of the SFD scheme that has been presented. A Penalty Score (PS) is computed by DFA-T based on the hazardous words that are included in the extracted URL. This score is determined by the properties of the URL. WC-NFS receives from PS the URL characteristics that were retrieved from DFA. After then, the WC accesses the URLs that are added to the WC-NFS in order to get the numeric value of the WC-Index (WCI). Both the current data set from social networking sites and appropriate machine learning methods are used in order to detect harmful URLs and posts. The malicious posts are identified using the data set. The suggested SFD distinguishes between benign and malicious URLs with a high mark of accurateness, as shown by the results of the experiments.