Anomaly detection using intelligent analytics - Chen, Zhaomin (2018)
Anomaly detection is an important issue which has been studied in diverse application domains. In computer networks, the anomalies could indicate possible attacks to the target server. Thus, it is necessary to establish fast detection of network anomalies in the server for the purpose of intrusion detection in the server side. In addition, in the bottleneck router, it is also necessary to detect and mitigate the Low-rate Denial of Service attacks. In the field of computer vision, anomaly detection is often referred to as outlier detection, which is also very important, especially for the deep learning techniques. Nowadays, deep learning has become a mainstream approach in many research pertaining to computer vision. Deep learning technique requires large datasets. Thus, outlier detection techniques are often applied here to clean the datasets. In this dissertation, the author focuses mainly on developing novel network anomaly detection and image outlier detection techniques.