Abstract

In recent years, online social networks (OSNs) have become a huge used platform for sharing activities, opinions, and advertisements. Spam content is considered one of the biggest threats in social networks. Spammers exploit OSNs for falsifying content as part of phishing, such as sharing forged advertisements, selling forged products, or sharing sexual words. Therefore, machine learning (ML) and deep learning (DL) techniques are the best methods for detecting phishing attacks and minimize their risk. This paper provides an overview of prior studies of OSNs spam detection modeling based on ML and DL techniques. The research papers are classified into three categories: the features used for prediction, the dataset size corresponding language used, real-time based applications, and machine learning or deep learning techniques. Challenges and opportunities in phishing attacks prediction using ML and DL techniques are also concluded in our study.