Showing papers by "Wentao Fan published in 2011"
11 Dec 2011
TL;DR: This paper proposes a novel unsupervised statistical approach for detecting network based attacks through finite generalized Dirichlet mixture models, in the context of Bayesian variational inference.
Abstract: In recent years, an increasing number of security threats have brought a serious risk to the internet and computer networks. Intrusion Detection System (IDS) plays a vital role in detecting various kinds of attacks. Developing adaptive and flexible oriented IDSs remains a challenging and demanding task due to the incessantly appearance of new types of attacks and sabotaging approaches. In this paper, we propose a novel unsupervised statistical approach for detecting network based attacks. In our approach, patterns of normal and intrusive activities are learned through finite generalized Dirichlet mixture models, in the context of Bayesian variational inference. Under the proposed variational framework, the parameters, the complexity of the mixture model, and the features saliency can be estimated simultaneously, in a closed-form. We evaluate the proposed approach using the popular KDD CUP 1999 data set. Experimental results show that this approach is able to detect many different types of intrusions accurately with a low false positive rate.
58 citations
13 Nov 2011
TL;DR: A variational framework of finite Dirichlet mixture models is proposed and applied to the challenging problem of object detection in static images and the performance of the proposed method is tested on challenging real-world data sets.
Abstract: In this paper, we propose a variational framework of finite Dirichlet mixture models and apply it to the challenging problem of object detection in static images. In our approach, the detection technique is based on the notion of visual keywords by learning models for object classes. Under the proposed variational framework, the parameters and the complexity of the Dirichlet mixture model can be estimated simultaneously, in a closed-form. The performance of the proposed method is tested on challenging real-world data sets.
12 citations
18 Dec 2011
TL;DR: The proposed model adopts a stick-breaking representation of the Dirichlet process and is learned through a variational inference method, and the determination of the number of clusters is sidestepped by assuming an infinitenumber of clusters.
Abstract: In this paper, we propose a Bayesian nonparametric approach for modeling and selection based on the mixture of Dirichlet processes with Dirichlet distributions, which can also be considered as an infinite Dirichlet mixture model The proposed model adopts a stick-breaking representation of the Dirichlet process and is learned through a variational inference method In our approach, the determination of the number of clusters is sidestepped by assuming an infinite number of clusters The effectiveness of our approach is tested on a real application involving unsupervised image categorization
9 citations