Network anomalies and attacks represent a serious challenge to ISPs, who need to cope
with an increasing number of unknown events that put their networks' integrity at risk. Most of …

Network anomaly detection has been a hot research topic for many years. Most detection
systems proposed so far employ a supervised strategy to accomplish the task, using either …

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 …

Anomaly detection has become a vital component of any network in today's Internet.
Ranging from non-malicious unexpected events such as flash-crowds and failures, to …

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 …

Most traditional network anomalies and attacks detection systems tend to employ supervised
strategies, which require labeled training dataset that is arduous and expensive to obtain …

Anomaly detection is a crucial topic in network security which refers to automatically mining
known and unknown attacks or threats. Many detectors have been proposed in the last …

Automatic identification of anomalies on network data is a problem of fundamental interest to
ISPs to diagnose incipient problems in their networks. ISPs gather diverse data sources from …

The growing hierarchical self organizing map (GHSOM) has been shown to be an effective
technique to facilitate anomaly detection. However, existing approaches based on GHSOM …

The unsupervised detection of network attacks represents an extremely challenging goal.
Current methods rely on either very specialized signatures of previously seen attacks, or on …