It is generally agreed that two key points always attract special concerns during the modelling of anomaly-based intrusion detection. One is the techniques about discerning two classes with different features, another is the construction/selection of the observed sample of normally occurring patterns for system normality characterization. In this paper, instead of focusing on the design of spec...

wireless sensor networks (wsns) arebecoming increasingly popular, and very useful in militaryapplications and environmental monitoring. however,security is a major challenge for wsns because they areusually setup in unprotected environments. our goal in thisstudy is to simulate an intrusion detection system (ids)that monitors the wsn and report intrusions accurately andeffectively. we have thus...

in order to obtain maximum information from magnetic and gravity anomaly maps, application of an edgedetection method is necessary. in this regard two commonly used methods are derivative filters and local phase filters. in this paper, a matlab code is expanded to combine an analytic signal filter and a tilt angle filter as a new edge detection filter called asta filter. this method was demonst...

To address one of the most challenging industry problems, we develop an enhanced training algorithm for anomaly detection in unlabelled sequential data such as time-series. We propose outputs a well-designed system are drawn from unknown probability distribution, U, normal conditions. introduce criterion based on classical central limit theorem that allows evaluation likelihood data-point is U....

In this paper we present an information fusion based distributed anomaly detection system for Android mobile phones. The proposed framework realizes a clientserver architecture, the client continuously extracts various features and transfers to the server, and the server’s major task is to detect anomaly using state-of-art detection algorithms implemented as anomaly detectors. Multiple distribu...

Intrusion detection is an integral part of computer security. It improves the security of information systems by allowing the review of patterns of access in order to discover abnormal activity of users and serving as a deterrent to users attempts to bypass system privilege or protection mechanisms. Anomaly detection systems, a subset of intrusion detection systems, model the normal system/netw...

This paper presents a statistical anomaly detection algorithm based on Markov chains. Our algorithm can be directly applied for intrusion detection by discovering anomalous activities. Our framework for constructing anomaly detectors is very general and can be used by other researchers for constructing Markov-chain-based anomaly detectors. We also present performance metrics for evaluating the ...

Health monitoring and anomaly detection techniques for artificial satellites are very significant, as it is very hard to repair those space systems on orbit. Authors have proposed the framework of learning-based anomaly detection that applies statistical machine learning and data mining techniques to the satellite telemetry data to automatically obtain normal behavior models which can be used f...

In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion distributions and propose a notion of “cut-bin” in histograms to distinguish unusual motions. For appearance anomaly detection, we develop a novel sch...