Applicability of Machine Learning in Spam Detection Systems

Abstract

In this age of information, online media is a growing reality. The main social media are Instagram, Facebook, and Twitter because these are social media networks that connect the world as other sources. Users who use online social media create and their own information independently as a result People who come across false information on social media can spread it further, by sharing it or otherwise engaging in it. Nowadays people spend a lot of time on social media online. As a result, it becomes an insightful place for analysis and to understanding people's perception on something. This popularity of social media attracts hackers and make them more likely to spam and spread misleading information, thus causing potential losses. Cyber-criminals are frequently hacked by producing criminal sites to steal sensitive external information or download malware. This has been a major issue for the security of social networking sites and has led to poor user experience. However, there is no suitable solution for detecting Twitter spam accurately. The methods available are mainly based on profiles and set up social honeypots to identify new social spam. In the proposed work, the main objective is to develop a robust Twitter spam detection system with adequate performance in detection and stability according to the huge amount of ground truth data. A logistic mutation-based genetic algorithm is proposed for the feature extraction and a Fuzzy decision tree combined with ANN has been used for classification. To calculate the system's performance, the detection accuracy, F-measure, the true positive rate/false positive rate also evaluated and compared with the existing framework.