A Survey on Online Social Network Anomaly Detection

Although many existing intrusion detection systems are based on signa- ture recognition techniques, anomaly detection techniques are better to detect novel intrusions or new

The authors first separate normal instances from anomalies in the training data, using frequent item-set mining, and then use the clustering-based technique to detect

An Unsupervised Anomaly Detection Framework for Detecting An Unsupervised Anomaly Detection Framework for Detecting Anomalies in Real Time through Network System’s Log Files

Recent papers adopt behavior technique for malicious detection, COMPA, an approach propose a way to detect compromised accounts behavioral profile and anomaly

Unlike previous unimodal GAN based anomaly detection [Schlegl et al.2017], [Zenati et al.2018], we use an Infinite Gaussian Mixture Model to detect anomalies in the latent space

A probabilistic algorithm called Probabilistic Auto Encoder based Anomaly Detection (PAE-AD) is proposed to detect anomalies and enhance processes involved.. Both

Keywords: clustering, intrusion detection, anomaly based, graph algorithm, DBSCAN

Another relevant category of work uses system metrics data, i.e., CPU utilizations, mem- ory consumption, etc., to detect software anomalies. The use of system metrics achieves