Networks of various kinds often experience anoma-lous behaviour. Examples include
attacks or large data transfers in IP networks, presence of intruders in distributed video …

Current network monitoring systems rely strongly on signa-ture-based and supervised-
learning-based detection methods to hunt out network attacks and anomalies. Despite being …

Anomaly detection in communication networks provides the basis for the uncovering of
novel attacks, misconfigurations and network failures. Resource constraints for data storage …

With the rapid rise in the ubiquity and sophistication of Internet technology and the
accompanying growth in the number of network attacks, network intrusion detection has …

Anomaly detection is a classic, long-term research problem. Previous attempts to solve it
have used auto-encoders to learn a representation of the normal behaviour of networks and …

Network anomaly detection is a critical aspect of network management for instance for QoS,
security, etc. The continuous arising of new anomalies and attacks create a continuous …

Network anomalies, circumstances in which the network behavior deviates from its normal
operational baseline, can be due to various factors such as network overload conditions …

Anomaly-based network intrusion detection systems (IDS) are valuable tools for the defense-
in-depth of computer networks. Unsupervised or unlabeled learning approaches for network …

Nowadays, network intrusion detectors mainly rely on knowledge databases to detect
suspicious traffic. These databases have to be continuously updated which requires …

In recent years, network anomaly detection has become an important area for both
commercial interests as well as academic research. Applications of anomaly detection …