LAUSR

The rapid development of online social networks such as Reddit, Twitter, Facebook, and so on are widely expanded. The anomaly detection plays an important role in social networks due to different kinds of malicious activities involving misleading crowdsourcing, contact lists, and spamming results with fake accounts. In this paper, Deep Belief Neural based Interactive Autodidactic School (DBN-IAS) algorithm is proposed to detect anomalies from social networks. Particularly, the novel IAS algorithm is used for optimization of optimal hidden layers from DBN. The experimental details are obtained from both benchmark and real-time datasets. Experimentally, the DBN-IAS approach is compared with other existing methods such as GCN, RBM + SVM with SDN, and LAD. Finally, the DBN-IAS algorithm demonstrated optimal anomaly detection results from social networks and the proposed algorithm shows the detection rate of 99.32%, which is far better than the existing approaches.