A Novel Anomaly Detection Scheme Based on Principal Component Classifier
نویسندگان
چکیده
Mei-Ling Shyu1∗, Shu-Ching Chen2, Kanoksri Sarinnapakorn1, LiWu Chang3 1Department of Electrical and Computer Engineering University of Miami, Coral Gables, FL, USA [email protected], [email protected] 2Distributed Multimedia Information System Laboratory School of Computer Science, Florida International University, Miami, FL, USA [email protected] 3Center for High Assurance Computer Systems, Naval Research Laboratory Washington, DC 20375, USA [email protected]
منابع مشابه
Detection of Mo geochemical anomaly in depth using a new scenario based on spectrum–area fractal analysis
Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate the blind and deep Mo anomaly in the Dalli Cu–Au porphyry mineralization area. The Dalli miner...
متن کاملA Novel Ensemble Approach for Anomaly Detection in Wireless Sensor Networks Using Time-overlapped Sliding Windows
One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data minin...
متن کاملDynamic anomaly detection by using incremental approximate PCA in AODV-based MANETs
Mobile Ad-hoc Networks (MANETs) by contrast of other networks have more vulnerability because of having nature properties such as dynamic topology and no infrastructure. Therefore, a considerable challenge for these networks, is a method expansion that to be able to specify anomalies with high accuracy at network dynamic topology alternation. In this paper, two methods proposed for dynamic anom...
متن کاملSparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains
In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...
متن کاملA New IRIS Segmentation Method Based on Sparse Representation
Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...
متن کامل