In this paper, we analyze an anomaly based intrusion detection system (IDS) for outlier detection in hardware profile using statistical techniques: Chi-square distribution, Gaussian mixture distribution and Principal component analysis. Anomaly detection based methods can detect new intrusions but they suffer from false alarms. Host based Intrusion Detection Systems (HIDSs) use anomaly detectio...

The number and impact of attack over the Internet have been continuously increasing in the last years, pushing the focus of many research activities into the development of effective techniques to promptly detect and identify anomalies in the network traffic. In this paper, we propose a performance comparison between two different histogram based anomaly detection methods, which use either the ...

In this paper, we present a statistical approach to anomaly detection and monitoring through image analysis, and its application in non-destructive evaluation. A non-parametric statistical model is created by Parzen window density estimation at each pixel location, based on the observations of a number of defect-free images and the derived low level features. A test image is compared against th...

Sociotechnological and geospatial processes exhibit time varying structure that make insight discovery challenging. To detect abnormal moments in these processes, a de€nition of ‘normal’ must be established. Œis paper proposes a new statistical model for such systems, modeled as dynamic networks, to address this challenge. It assumes that vertices fall into one of k types and that the probabili...

Cluster ensembles aim to find better, more natural clusterings by combining multiple clusterings. We apply ensemble clustering to anomaly detection, hypothesizing that multiple views of the data will improve the detection of attacks. Each clustering rates how anomalous a point is; ratings are combined by averaging or taking either the minimum, the maximum, or median score. The evaluation shows ...

This study examines the application of cluster analysis in the accounting domain, particularly discrepancy detection in audit. Cluster analysis groups data so that points within a single group or cluster are similar to one another and distinct from points in other clusters. Clustering has been shown to be a good candidate for anomaly detection. The purpose of this study is to examine the use of...

Anomaly detection is a popular problem in many fields. We investigate an anomaly detection method based on probability density function (PDF) of different status. The constructed PDF only require few training data based on Kullback–Leibler Divergence method and small signal assumption. The measurement matrix was deduced according to principal component analysis (PCA). And the statistical detect...

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 theme of this paper is that anomaly detection splits into two parts: developing the right features, and then feeding these features into a statistical system that detects anomalies in the features. Most literature on anomaly detection focuses on the second part. Our goal is to illustrate the importance of the first part. We do this with two real-life examples of anomaly detectors in use at ...

The (randomized) real-valued negative selection algorithm is an anomaly detection approach, inspired by the negative selection immune system principle. The algorithm was proposed to overcome scaling problems inherent in the hamming shape-space negative selection algorithm. In this paper, we investigate termination behavior of the realvalued negative selection algorithm with variable-sized detec...