Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

B Prabhu Kavin,Sulaima Lebbe Abdul Haleem,S Hemalatha,Amsalu Gosu Adigo,Vineet Tirth,Deepa Jose,M Thangamani,Pravin R Kshirsagar,Deepmala Singh,Sagar Karki,R Vijayalakshmi

doi:10.1155/2022/6356152

Abstract

Social media websites are becoming more prevalent on the Internet. Sites, such as Twitter, Facebook, and Instagram, spend significantly more of their time on users online. People in social media share thoughts, views, and facts and create new acquaintances. Social media sites supply users with a great deal of useful information. This enormous quantity of social media information invites hackers to abuse data. These hackers establish fraudulent profiles for actual people and distribute useless material. The material on spam might include commercials and harmful URLs that disrupt natural users. This spam content is a massive problem in social networks. Spam identification is a vital procedure on social media networking platforms. In this paper, we have proposed a spam detection artificial intelligence technique for Twitter social networks. In this approach, we employed a vector support machine, a neural artificial network, and a random forest technique to build a model. The results indicate that, compared with RF and ANN algorithms, the suggested support vector machine algorithm has the greatest precision, recall, and F-measure. The findings of this paper would be useful in monitoring and tracking social media shared photos for the identification of inappropriate content and forged images and to safeguard social media from digital threats and attacks.

Highlights

In the last few years, online social networks (OSNs), including Facebook, Twitter, and LinkedIn, are becoming extremely common
The main purpose of this paper is to measure the performance of the spam detection classification models on Twitter
This paper gives a systematic analysis of essential approaches for identifying fraudulent accounts on online social networking sites, such as Facebook (OSNs)

Summary

Introduction

In the last few years, online social networks (OSNs), including Facebook, Twitter, and LinkedIn, are becoming extremely common. The false accounts use their accounts for multiple aims, including circulating rumors that impact a certain economy or even culture as a wider market. Twitter is a large type of online communication that probably contains vast knowledge which opens up new opportunities for tweet content analysis. 74 per majority of people state that either the “lacking of IT infrastructure” or an overarching cost-benefit study is the main barrier to use technology. Despite these obstacles, technology appears to be gradually being embraced. More than half of the insurers analyzed said in the last five years, several in the last two years, they have been utilizing antifraud technology solutions [2, 3]

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