Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Efficiently detecting network intrusions requires the gathering of sensitive information. This
means that one has to collect large amounts of network transactions including high details of …

Network based communication is more vulnerable to outsider and insider attacks in recent
days due to its wide spread applications in many fields. Intrusion Detection System (IDS) a …

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 …

Outlier detection has been a very important concept in the realm of data analysis. Recently,
several application domains have realized the direct mapping between outliers in data and …

It is a well-known problem that intrusion detection systems overload their human operators
by triggering thousands of alarms per day. This paper presents a new approach for handling …

Unlike signature or misuse based intrusion detection techniques, anomaly detection is
capable of detecting novel attacks. However, the use of anomaly detection in practice is …

This study discusses the application of deep learning technology in network intrusion
detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the …

STATL is an extensible state/transition-based attack description language designed to
support intrusion detection. The language allows one to describe computer penetrations as …