Deep Learning Based IDS to Detect Anomaly Over Social Networking Site: Comprehensive Review

One of the primary purposes of an Intrusion detection system (IDS) is to categorize the regular and irregular activities on the online social network (OSN). The complexity of patterns of communication in OSN makes the intrusion detection a challenging task as compared to traditional networks. Recent work aims at soft computing technique based intrusion detection. This paper is an analytical study of emerging deep learning techniques which will assist the researchers to identify normal and abnormal behavior of users on any social networking site. We propose to use the Decision Tree Classifier followed by histogram of oriented gradients (HOG) respectively for feature extraction and representation followed by training on a deep Convolution Network to detect the presence of anomalies. Performance measures, generic datasets and hybrid techniques are also suggested to evaluate deep learning approaches in IDS for OSN.