Sequential Feature Explanations for Anomaly Detection
نویسندگان
چکیده
منابع مشابه
Sequential Feature Explanations for Anomaly Detection
In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g. a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anomalous, leaving the analyst with no guidance about where to begin the investiga...
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Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...
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A feature-based approach for detecting anomalies in spectral, spatial, temporal, and other domains is described. When the frequency of occurrence is small relative to the background, anomalies such as man-made objects in natural image backgrounds do not form their own clusters, but are instead assigned the nearest background cluster, becoming an outlier (statistical anomaly) in that cluster. Ou...
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Early, James P. Ph.D., Purdue University, August, 2005. Behavioral Feature Extraction for Network Anomaly Detection. Major Professors: Carla E. Brodley and Eugene H. Spafford. This dissertation presents an analysis of the features of network traffic commonly used in network-based anomaly detection systems. It is an examination designed to identify how the selection of a particular protocol attr...
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ژورنال
عنوان ژورنال: ACM Transactions on Knowledge Discovery from Data
سال: 2019
ISSN: 1556-4681,1556-472X
DOI: 10.1145/3230666