Tag Archives: AnomalyDetection

Statistical Evaluation of Artificial Intelligence -Based Intrusion Detection System

Training neural networks with captured real-world network data may fail to ascertain whether or not the network architecture is capable of learning the types of correlations expected to be present in real data. In this paper we outline a statistical model aimed at assessing the learning capability of neural network-based intrusion detection system. We explore […]

Network Anomaly Detection Based on WaveNet

Increasing amount of attacks and intrusions against networked systems and data networks requires sensor capability. Data in modern networks, including the Internet, is often encrypted, making classical traffic analysis complicated. In this study, we detect anomalies from encrypted network traffic by developing an anomaly based network intrusion detection system applying neural networks based on the […]

On Application-Layer DDoS Attack Detection in High-Speed Encrypted Networks

Application-layer denial-of-service attacks have become a serious threat to modern high-speed computer networks and systems. Unlike network-layer attacks, application-layer attacks can be performed by using legitimate requests from legitimately connected network machines which makes these attacks undetectable for signature-based intrusion detection systems. Moreover, the attacks may utilize protocols that encrypt the data of network connections […]