DeepOSN: Bringing deep learning as malicious detection scheme in online social network

Putra Wanda (Harbin University of Science and Technology)
Marselina Endah Hiswati (University of Respati Yogyakarta)
Huang J. Jie (Harbin University of Science and Technology)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 9, No 1: March 2020

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.