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

Wanda, PutraHiswati, Marselina EndahJ. Jie, Huang
IAES International Journal of Artificial Intelligence (IJ-AI) (Sinta 1)Vol. 0 No. 01 Maret 2020
DOI10.11591/ijai.v9.i1.pp146-154

Abstrak

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.

Kata Kunci

Neural Network, Deep Learning, Mobile ComputingDeep LearningSocial Network, Malicious DetectionSecurity and Privacy

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DeepOSN: Bringing deep learning as malicious detection scheme in online social network | IAES International Journal of Artificial Intelligence (IJ-AI) | Publiora