Assuring secure and reliable operation of networks has become a priority research area these days because of ever growing dependency on network technology. Intrusion detection systems (IDS) are used as the last line of defence. IDS identifies patterns of known intrusions (misuse detection) or differentiates anomalous network data from normal data (anomaly detection). In this paper, a novel Intr...

The exponential growth in wireless network faults, vulnerabilities, and attacks make the WLAN security management a challenging research area [29]. Data mining applied to intrusion detection is an active area of research. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require proces...

English) The immune system is a complex system which protects humans and animals against diseases caused by foreign intruders such as viruses, bacteria and fungi. It appears as if the recognition and protection mechanism of the immune system can lead to the development of novel concepts and techniques for detecting intrusions in computer networks, particularly in the area of anomaly detection. ...

English) The immune system is a complex system which protects humans and animals against diseases caused by foreign intruders such as viruses, bacteria and fungi. It appears as if the recognition and protection mechanism of the immune system can lead to the development of novel concepts and techniques for detecting intrusions in computer networks, particularly in the area of anomaly detection. ...

This chapter presents an extension of conformal prediction for anomaly detection applications. It includes the presentation and discussion of the Conformal Anomaly Detector (CAD) and the computationally more efficient Inductive Conformal Anomaly Detector (ICAD), which are general algorithms for unsupervised or semi-supervised and offline or online anomaly detection. One of the key properties of...

Methods: This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares feat...