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 …

The philosophy of traffic monitoring for detection of network attacks is based on an acquired
knowledge perspective: current techniques detect either the well-known attacks on which …

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 …

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 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 …

Insider attack detection in an enterprise network environment is a critical problem that
currently has no promising solution. It represents a significant challenge since host …

In this study, we apply an anomaly-based approach to analyze traffic flows transferred over a
network to detect the flows related to different types of attacks. Based on the information …

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 …

Traditional Network Intrusion Detection Systems (NIDSs) rely on either specialized
signatures of previously seen attacks, or on expensive and difficult to produce labeled traffic …