Abstract. Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations

scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is proposed for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γ

, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as \How many clusters are there?", "Which clustering method should be used?" and \How should outliers be handled?". We outline a general methodology

this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.

2.Neural network 3.Machine learning 2. Intrusion detection 1.Categories 2.Algorithmic techniques 3. Anomaly detection techniques in IDS 1.A novel anomaly detection technique using Kohonen network 2.Conclusions from the tests conducted on multi-level K-map

The present invention is a new real-time approach to detect aberrant modes of system behavior induced by abnormal and unauthorized system activities. This detection methodology is novel in that it is based on behavioral information obtained from a suitably instrumented computer program

Scanning of ports on a computer occurs frequently on the Internet. An attacker performs port scans of IP addresses to find vulnerable hosts to compromise. However, it is also useful for system administrators and other network defenders to detect port scans as possible preliminaries to more serious

Abstract. This paper identifies baseline procedures for verifying software for individual, small team, and large team development efforts for mission-critical and non-mission-critical software. It is based on defect-based inspections and basis path testing. Basis path testing provides a unified approach for performing unit, integration, and functional tests, whereas defect-based inspections are primarily used for verifying requirement and design documents. However, in situations where practitioners cannot afford to be as thorough as basis path testing permits, several heuristics are defined for prioritizing the remaining verification efforts and deciding which technique to apply. In addition, several studies are discussed that identify the relative merit of various verification tech-niques.

occlusion. In evaluating object detection approaches, several important methodological issues arise that have not been satisfactorily addressed in previous work. A secondary focus of this paper is to highlight these issues and to develop rigorous evaluation standards for the object detection problem. A

When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection