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As the object-based storage device (OSD) protocol emerges as the next generation storage technology, its security has received a great deal of attentions. Object-based storage security mechanism is credential-based. It doesn't consider intrusion threat. Since OSD can observe all the changes to the object data and attributes, it can spot several types of intrusion. In this paper, we study how intrusion...
Most current intrusion detection system employ signature-based methods that rely on labeled training data, however, in practice, this training data is typically expensive to produce. In contrast, unsupervised anomaly detection has great utility within the context of network intrusion detection system. Such a system can work without the need for massive sets of pre-labeled training data. Thus, with...
This article presents a masquerade detection system based on correlation eigen matrix and support vector machine (SVM). The system first creates a profile defining a normal user's behavior by correlation eigen matrix, and then compares the similarity of a current behavior with the created profile to decide whether the input instance is valid user or masquerader. In order to avoid overfitting and reduce...
It is well-known that current intrusion detection systems produce large numbers of false alerts. Those low quality alerts make it very hard for administrators to understand and take appropriate actions. To deal with false positive, in this paper, an attack-feedback-based approach is introduced to verify the success of attacks. This method processes each packet as soon as it is received. When a suspect...
The rival penalized expectation-maximization (RPEM) algorithm has demonstrated its powerful capability to perform the model selection automatically in the context of mixture model. However, the performance may be degraded when irrelevant variables are included. To overcome this drawback, we adopt the concept of feature salience as the feature weight to measure the relevance to the clusters in the...
The detection of counterfeit in printed documents is currently based mainly on built-in security features or on human expertise. We propose a classification system that supports non-expert users to distinguish original documents from PC-made forgeries by analyzing the printing technique used. Each letter in a document is classified using a support vector machine that has been trained to distinguish...
We propose a snake-based object segmentation algorithm for pairs of stereo images. Unlike previously developed snake-based algorithms, the algorithm in this paper performs well even when the background is cluttered. Moreover, the algorithm can successfully extract the contour of an object even in the presence of boundary concavities, and this algorithm is not sensitive to the placement of initial...
Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that can extract parts from visual data. The goal of this technique is to find intuitive basis such that training examples can be faithfully reconstructed using linear combination of basis images which are restricted to non-negative values. Thus NMF basis images can be understood as localized features that correspond better...
Dempster's rule of combination, a classical combination rule with several interesting mathematical properties, is widely employed. However, as an inherent problem, Dempster's rule of combination is incapable of managing the existing conflicts from various information sources at the process of normalization. The conflict management becomes a crucial problem in the operation of combination, especially...
A novel feature based on the combination of gradient feature and coefficients of wavelet transform is developed in this paper. In handwritten character recognition, the gradient feature represents local characteristic properly, but it is sensitive to the deformation of handwritten character. Meanwhile, wavelet transform represents the character image in multiresolution analysis and keeps adequate...
This paper presents a novel method for feature extraction based on the generalized entropy of the histogram formed by Euclidean distances, which is named distributive entropy of Euclidean distance (DEED in sort). DEED is a nonlinear measure for learning feature space, which provides the congregate and information measure of learning samples space. The ratio of between-class DEED to within-class DEED...
Several novel methods for nonlinear dimensionality reduction, named as manifold learning, have been proposed recently and widely used in pattern recognition and machine learning. In this paper, we present three face recognition methods based on kernel Isomap, which is a representative manifold learning method using kernel trick. Considering the class label by adjusting the Euclidean distance using...
The nearest feature line (NFL), feature plane (NFP) and feature subspace (NFS) classifiers have achieved good results in face recognition. However, in these three methods the facial features need to be extracted before classification can be performed. To overcome this drawback, in this paper we extend these three classifiers to kernel based NFL, NFP and NFS classifiers respectively. In addition, two...
This paper presents a novel algorithm of correspondence matching of point-sets by using Laplacian spectra of graphs. We make three contributions. Firstly, according to the two point sets to be matched, we define a Laplacian matrix with Euclidean distance, and give a closed form solution in terms of the matching matrix constructed on the vectors of eigenspace of the Laplacian matrix. Secondly, we theoretically...
A novel model for independent radial basis function (IRBF) neural network employing Gabor-based kernel PCA with fractional power polynomial models for feature extraction is proposed in this paper. In the new model, a bank of Gabor filters is first built to extract Gabor face representations characterized by selected frequency, locality and orientation to cope with various illuminations, facial expression...
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results...
Target recognition based on high range resolution (HRR) polarized radar using support vector machines (SVMs) was studied in this paper. A fuzzy membership function was constructed based on SVM decision-making function in order to improve the performance of OAA and OAO classifiers for multi-class target, and HRR radar target recognition method using improved SVM was proposed: First, the polarized radar...
It is well-known that the distribution of face images with different pose, illumination and face expression is complex and nonlinear. The traditional linear methods, such as linear discriminant analysis (LDA), will not give a satisfactory performance. In addition, LDA always suffers from small sample size (S3) problem, which always occurs when the sample size is smaller than the dimensionality of...
This paper studied tracking models of air crafts, pointed out that the essence of radar tracking is to ascertain the relative bearing and elevation angular motion of the target promptly and exactly. To the question of dissatisfied with single model tracking, we modified the "current" model, and proposed a kind of single adaptive model to track angular motion. We also gave the algorithm of...
This paper introduces a novel face recognition method based on DT-CWT feature representation using ONPP. The dual-tree complex wavelet transform (DT-CWT) used for representation features of face images, whose kernels are similar to Gabor wavelets, exhibit desirable characteristics of spatial locality and orientation selectivity. And DT-CWT outperforms Gabor with less redundancy and much efficient...
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