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Pattern recognition
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The article was published on 1998-11-16 and is currently open access. It has received 766 citations till now. The article focuses on the topics: Pattern recognition (psychology).read more
Citations
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Proceedings Article
EXPOSURE : Finding malicious domains using passive DNS analysis
TL;DR: This paper introduces EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in malicious activity, and uses 15 features that it extracts from the DNS traffic that allow it to characterize different properties of DNS names and the ways that they are queried.
Journal ArticleDOI
Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding
TL;DR: In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal, and it is shown that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the E MD decomposition mode.
Journal ArticleDOI
Comparative study on classifying human activities with miniature inertial and magnetic sensors
TL;DR: Bayesian decision making (BDM) results in the highest correct classification rate with relatively small computational cost, and a performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost.
Proceedings ArticleDOI
Disclosure: detecting botnet command and control servers through large-scale NetFlow analysis
TL;DR: This paper presents Disclosure, a large-scale, wide-area botnet detection system that incorporates a combination of novel techniques to overcome the challenges imposed by the use of NetFlow data, and identifies several groups of features that allow Disclosure to reliably distinguish C&C channels from benign traffic using NetFlow records.
Journal ArticleDOI
Two-Stage Pattern Recognition of Load Curves for Classification of Electricity Customers
TL;DR: In this article, a two-stage methodology was developed for the classification of electricity customers, based on pattern recognition methods, such as k-means, Kohonen adaptive vector quantization, fuzzy kmeans and hierarchical clustering, which are theoretically described and properly adapted.
References
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Journal ArticleDOI
ECG-based heartbeat classification for arrhythmia detection
TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.
Proceedings Article
EXPOSURE : Finding malicious domains using passive DNS analysis
TL;DR: This paper introduces EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in malicious activity, and uses 15 features that it extracts from the DNS traffic that allow it to characterize different properties of DNS names and the ways that they are queried.
Journal ArticleDOI
Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding
TL;DR: In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal, and it is shown that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the E MD decomposition mode.
Journal ArticleDOI
Comparative study on classifying human activities with miniature inertial and magnetic sensors
TL;DR: Bayesian decision making (BDM) results in the highest correct classification rate with relatively small computational cost, and a performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost.
Proceedings ArticleDOI
Disclosure: detecting botnet command and control servers through large-scale NetFlow analysis
TL;DR: This paper presents Disclosure, a large-scale, wide-area botnet detection system that incorporates a combination of novel techniques to overcome the challenges imposed by the use of NetFlow data, and identifies several groups of features that allow Disclosure to reliably distinguish C&C channels from benign traffic using NetFlow records.