Yajian Zhou

A distance sum-based hybrid method for intrusion detection

Intrusion detection systems based on a hybrid approach have attracted considerable interest from researchers. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. In this research, we propose a new and easy-to-implement hybrid learning method, named distance sum-based support vector machine (DSSVM), which can be used as an effective intrusion detection model. In DSSVM, we introduce the distance sum, a correlation between each data sample and cluster centers. Consider a data set represented by n-dimensional feature vectors, each distance sum for a data sample in the data set is obtained from the distances between this data sample and k−1 of k cluster centers found by a clustering algorithm. A new data set representing the features of these distance sums is formed and used to train a support vector machine classifier. By applying DSSVM to the KDD’99 data set, our experimental results show that the proposed hybrid method performs well in both detection performance and computational cost, which suggests it is a competitive candidate for intrusion detection. In addition, we also use six databases with different numbers of features, classes, and data samples to further validate the effectiveness of our method for some other pattern recognition problems.

Convex Decomposition Based Cluster Labeling Method for Support Vector Clustering

Support vector clustering (SVC) is an important boundary-based clustering algorithm in multiple applications for its capability of handling arbitrary cluster shapes. However, SVC’s popularity is degraded by its highly intensive time complexity and poor label performance. To overcome such problems, we present a novel efficient and robust convex decomposition based cluster labeling (CDCL) method based on the topological property of dataset. The CDCL decomposes the implicit cluster into convex hulls and each one is comprised by a subset of support vectors (SVs). According to a robust algorithm applied in the nearest neighboring convex hulls, the adjacency matrix of convex hulls is built up for finding the connected components; and the remaining data points would be assigned the label of the nearest convex hull appropriately. The approachs validation is guaranteed by geometric proofs. Time complexity analysis and comparative experiments suggest that CDCL improves both the efficiency and clustering quality significantly.

Fast and scalable support vector clustering for large-scale data analysis

As an important boundary-based clustering algorithm, support vector clustering (SVC) benefits multiple applications for its capability of handling arbitrary cluster shapes. However, its popularity is degraded by both its highly intensive pricey computation and poor label performance which are due to redundant kernel function matrix required by estimating a support function and ineffectively checking segmers between all pairs of data points, respectively. To address these two problems, a fast and scalable SVC (FSSVC) method is proposed in this paper to achieve significant improvement on efficiency while guarantees a comparable accuracy with the state-of-the-art methods. The heart of our approach includes (1) constructing the hypersphere and support function by cluster boundaries which prunes unnecessary computation and storage of kernel functions and (2) presenting an adaptive labeling strategy which decomposes clusters into convex hulls and then employs a convex-decomposition-based cluster labeling algorithm or cone cluster labeling algorithm on the basis of whether the radius of the hypersphere is greater than 1. Both theoretical analysis and experimental results (e.g., the first rank of a nonparametric statistical test) show the superiority of our method over the others, especially for large-scale data analysis under limited memory requirements.

Efficient representation of text with multiple perspectives

Abstract An effective text representation scheme dominates the performance of text categorization system. However, based on the assumption of independent terms, the traditional schemes which tediously use term frequency (TF) and document frequency (DF) are insufficient for capturing enough information of a document and result in poor performance. To overcome this limitation, we investigate exploring the relationships between different terms of the same class tendency and the way of measuring the importance of a repetitive term in a document. In this paper, a group of novel term weighting factors are proposed to enhance the category contribution for each term. Then, based on a novel strategy of generating passages from document, we present two schemes, the weighted co-contributions of different terms corresponding to the class tendency and the weighted co-contributions for each term in different passages, to achieve improvements on text representation. The prior scheme works in a dimensionality reduction mode while the second one runs in the conventional way. By employing the support vector machine (SVM) classifier, experiments on four benchmark corpora show that the proposed schemes could achieve a consistent better performance than the conventional methods in both efficiency and accuracy. Further analysis also confirms some promising directions for the future works.

A novel term weighting scheme with distributional coefficient for text categorization with support vector machine

In text categorization, vectorizing a document by probability distribution is an effective dimension reduction way to save training time. However, the data sets that share many common keywords between categories affect the classification performance seriously. To address that problem, firstly, we conduct an effective term weighting scheme consisting of posterior probability and relevance frequency to improve the performance of the traditional hybrid classification model. To get a better representation of the information contained in a document, as well as the introduction of an advanced hybrid classification model, we also propose a novel term weighting scheme with distributional coefficient so as to obtain further accuracy enhancement. The experimental results show that these proposed schemes are significantly better than the traditional method.

Research on IPSec-based NAT-PT transition mechanism

Similar to conventional NAT technology, NAT-PT gateways break traditional TCP/IPs end-to-end argument property which result in IPSec can not be applied in NAT-PT environment, and would fall flat when the pool of IPv4 addresses is exhausted. A solution by adding IP transform message, modifying the address mapping tables and session tables, using port transform strategy with inner host computer character in IKE negotiation was proposed which implemented bidirectional communication among the nodes of IPv4 and IPv6, and made NAT-PT compatible with ESP and AH. Performance analysis shows that the proposed scheme is feasible and effective.

A novel model for query over encrypted XML databases

In recent years, there has been increasing concern about “Database as a Service”(DAS) architectures. Data security is the overriding concern in DAS, and encryption is a natural solution. However, queries over encrypted databases are usually inefficient due to a heap of time on the encryption and decryption. In this paper, we present an encryption scheme and XQuery translation model of XML database. The major work and contribution of this paper are as below: 1) Splitting method is proposed in the encryption scheme, in which more flexible encryption granularity is obtained. 2) We put forward the encryption strategy that, by encrypting two or more fragments together, is efficient to resist various kinds of database attack owing to the changed ciphertext distribution and data size by splitting. 3) An XQuery translation model is introduced, by converting the query of XML data into that of relational data, combined with XML Schemas Compression and hash technology, which perform the XQuery language efficiently. Our experimental evaluation shows that our XML database encryption and query model achieves both excellent query efficiency and robust security.

Exposing photo manipulation with inconsistent perspective geometry

Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value (NMV) and normalized standard deviation (NSD) of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.

Universal composable secure protocol for EPC system

As an important component of internet of things, electronic product code (EPC) system is widely used in many areas. However, the mass deployment of EPC system is frequently degraded by security and privacy problems. Therefore, the major researches focus on the design of a secure EPC system with high efficiency. This paper discusses the security requirements of EPC system and presents a universal composable (UC) model for EPC system, the ideal functionality of EPC system is also formally defined with the UC framework. Then a secure protocol for EPC system under UC framework is proposed and the analysis of security and performance of the proposed protocol is given, in comparison with other protocols, the results show that the proposed protocol is UC secure and can provide privacy protection, untraceability, authorized access, anonymity and concurrent security for EPC system. Furthermore, less computation and storage resource are required by the proposed protocol.

Variational learning for finite Beta-Liouville mixture models

Abstract In the article, an improved variational inference (VI) framework for learning finite Beta-Liouville mixture models (BLM) is proposed for proportional data classification and clustering. Within the VI framework, some non-linear approximation techniques are adopted to obtain the approximated variational object functions. Analytical solutions are obtained for the variational posterior distributions. Compared to the expectation maximization (EM) algorithm which is commonly used for learning mixture models, underfitting and overfitting events can be prevented. Furthermore, parameters and complexity of the mixture model (model order) can be estimated simultaneously. Experiment shows that both synthetic and real-world data sets are to demonstrate the feasibility and advantages of the proposed method.