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

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.

Challenges and Solutions of the Application of Data-Driven Technology in Network 3D Game Design

“…However, with the continuous development of website technology and operations, people have increasingly data requirements. To achieve more sophisticated operations to improve the quality of the website, the data acquisition method of the website also develops continuously with the progress of website technology and the deepening of people's demand for website data [10]. From the perspective of use and development, it is mainly divided into three categories: website log fles, web beacons (commonly known as dots), and JS page tags.…”

Uma Abordagem para Detecção Automática de Fraudes em Aplicativos de Mensagens Instantâneas

“…Em [Prabhu Kavin et al 2022], os autores propõem uma abordagem baseada na inteligência artificial para detecc ¸ão automática de perfis falsos (ou Spamers) no Twitter. Eles exploraram três algoritmos de classificac ¸ão diferentes: Support Vector Machine (SVM), Random Forest (RF) e Multilayer Perceptron (MLP).…”

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

“…In their research, Page et al 121 used nine web‐based features and six domain‐based features in their ML method for detecting phishing domains. Last few years, several works have been proposed using ML 122‐129 …”

“…Siva Rama Krishna et al 171 used the KNN algorithm to identify cloned or fake accounts and compared the results with the naive Bayesian algorithm. In their paper, Prabhu Kavin et al 126 proposed an AI technique using a support vector machine and ANN algorithms to detect fake accounts and spam content in social media. Analyzing various characteristics, Sahoo and Gupta 172 discussed a chrome extension‐based framework for detecting fake accounts in the Twitter environment 173‐175 also used ML based approach to detect fake or clone accounts in social networks.…”