ABSTRACT
As social media use increases, the number of users has risen also. This has increased the volume of data carried over the network, making it more important to secure users' data and privacy from threats. As users are unaware of hackers, social media's security flaws and new forms of attack will persist. Intrusion detection systems, therefore, are vital to identifying intrusion risks. This paper examines a variety of intrusion detection techniques used to detect cyberattacks on social media networks. The paper provides a summary of the prevalent attacks on social media networks, such as phishing, fake profiles, account compromise, and cyberbullying. Then, the most prevalent techniques for classifying network traffic, including statistical and artificial intelligence (AI) techniques, are addressed. The literature also demonstrates that because AI can manage vast, scalable networks, AI-based IDSs are more effective at classifying network traffic and detecting intrusions in complex social media networks. However, AI-based IDSs exhibit high computational and space complexities;therefore, despite their remarkable performance, they are more suitable for high computing power systems. Hybrid IDSs, utilizing statistical feature selection and shallow neural networks, may provide a compromise between computational requirements and efficiency. This investigation shows that accuracies of statistical techniques range from 90% to 97.5%. In contrast, AI and ML technique detection accuracy ranges from 78% to 99.95%. Similarly, swarm and evolutionary techniques achieved from 84% to 99.95% and deep learning-based detection techniques achieved from 45% to more than 99% detection rates. Convolutional neural network deep learning systems outperformed other methods due to their ability to automatically craft the features that would classify the network traffic with high accuracy.