Bot Net Detection for Social Media Using Segmentation with Classification Using Deep Learning Architecture

Abstract

The use of bots on social media raises serious questions about the reliability and authenticity of the content. Currently, there are numerous methods for detecting bots. However, there is still room for improvement in the detection's accuracy.This research propose novel technique in BotNet detection for social media networks based on segmentation and classification utilizing DL technique. Input is collected as BotNet based social media and processed for noise removal and smoothening. The segmentation of processed data is carried out using Fuzzy-C means clustering and feature extracted using Multi layered Convolutional Neural Network (MLCNN). The experimental analysis has been performed in terms of RMSE, F 1 score, recall, accuracy, and precision.The suggested solution offers a machine learning-based bot identification technique that is more precise and efficient. The research makes use of a number of strategies and methods that improve the effectiveness of bot detection and removal.