With the hyperspectral imaging technology, data provides abundant spectral information and plays a more important role in geological survey, vegetation analysis, military reconnaissance. Different from normal change detection, anomaly detection (HACD) helps to find those small but changes between multitemporal images (HSI). In previous works, most classical methods use linear regression establi...

The goal of a network-based intrusion detection system (IDS) is to identify patterns of known intrusions (misuse detection) or to differentiate anomalous network activity from normal network traffic (anomaly detection). Data mining methods have been used to build automatic intrusion detection systems based on anomaly detection. The goal is to characterize the normal system activities with a pro...

We introduce a new approach to anomaly detection based on extreme value theory statistics. Our method improves detection accuracy by replacing binary feature thresholds with anomaly scores and by modeling the tail region of the distribution where anomalies occur. It requires no optimization or tuning and provides insights into results. This work describes the Extreme Value Theory-Anomaly Detect...

The syntax of application layer protocols carries valuable information for network intrusion detection. Hence, the majority of modern IDS perform some form of protocol analysis to refine their signatures with application layer context. Protocol analysis, however, has been mainly used for misuse detection, which limits its application for the detection of unknown and novel attacks. In this contr...

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 defense. Intrusion Detection System identifies patterns of known intrusions (misuse detection) or differentiates anomalous network data from normal data (anomaly detection). In t...

Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly detection. In this paper Machine learning based methods which are one of the types of Anomaly detection techniques is discussed.

We have applied an automatic interpretation method of potential data called AN-EUL in unexploded ordnance (UXO) prospective which is indeed a combination of the analytic signal and the Euler deconvolution approaches. The method can be applied for both magnetic and gravity data as well for gradient surveys based upon the concept of the structural index (SI) of a potential anomaly which is relate...

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...

A taxonomy was developed by Axelsson to define the space of intrusion detection technology and classify IDSs. The taxonomy categorizes IDSs by their detection principle and their operational aspects. The two main categories of detection principles are signature detection and anomaly detection. The remainder of this paper will compare the two categories of detection principles and describe a new...