1262783
An Immunity-Based Intrusion Detection Solution for Database Systems
Database intrusion detection has been an important research area in database security. It focuses on malicious transaction attacks, which cannot be handled by traditional database security mechanisms, such as authorization, access control, integrity control, and so on. Although there have appeared some intrusion detection systems, current researches on malicious transaction detection are limited in accuracy and efficiency. Inspired by natural immune system, we propose a novel immunity-based intrusion detection solution for database system in this paper. It provides an additional layer of defense against DBMS misuse, especially malicious transactions. The ability to learn and to adapt to the environment dynamically entitles the system to detect both known and unknown malicious transaction intrusions efficiently. Simulations show that the database intrusion detection system based on data immunity can accelerate detection of malicious transaction attacks and improve its accuracy without causing any other performance penalty.
5747087
Efficient Processing of Warping Time Series Join of Motion Capture Data
Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of l-e-join over two time series. Given two time series, the goal of l-e-join is to find those non-trivial matching subsequences by detecting maximal l-connections from the e-matching matrix of the two time series. Given a querying motion sequence, the l-e-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-e-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the e-matching matrix where there are no maximal l-connections without incurring costly computation. The refinement step serves to detect closed l-connections within regions that cannot be pruned by the filtering step. To speed up the computation of e-matching matrix, we propose a block-based time series summarization method, based on which the block-wise e-matching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the block-wise e-matching matrix.Extensive experiments on l-e-join of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing l-e-join of motion capture time series.
18102767
Resilient Rights Protection for Product Data
With the development of advance manufacture applications, enterprise must interact with their suppliers and customers to share more product data information. Consequently, copyright and integrity protection over product data is becoming an urgent requirement. Product data information consists of two parts: product structure and part content, besides protecting the three-dimensional (3D) model of part by watermarking, we also need to protect product structure information, which describes the relations among product parts and can be represented as a tree. In this paper, based on the 3D polygonal meshes watermark technology, the structure/semi-structure data watermark technologies and the idea of Fractal, we propose a novel watermark scheme for tree-structure product data with the value lying both in product structure and in part node content, which gives a more comprehensive and resilient right protection for both part node content and product structure. Moreover it is particularly effective when resisting invertibility attacks
15447660
A logical replication-based intrusion detection approach for CSCW system
With the rapid development of CSCW techniques, CSCW system security has been of much attention. Its major challenge comes from malicious attacks, which cannot be handled by traditional security mechanisms, such as authorization, access control, encryption, etc. Although there existed some intrusion detection systems, current researches on intrusion detection are still insufficient in accuracy and efficiency for two reasons: firstly, most of them implement malicious transaction detection only by matching samples with predefined patterns; secondly, most current researches mainly resort to semantic analyzing or even only manual interventions to judge the malicious attacks. Inspired by the replication technique, we propose and implement a novel logical replication-based intrusion detection solution for CSCW system in this paper. It provides an additional layer of defense against application attacks, especially program attacks. The approach entitles the system to detect malicious intrusions efficiently at low false positive rate.
17533153
OTJFast: Processing Ordered XML Twig Join Fast
Thanks to standard specifications for Web services, more and more data is represented and exchanged in XML. Efficiently finding all occurrences of a twig pattern is a core operation of XML query processing. Recently, a large number of holistic twig join algorithms have been proposed, however ordered XML twig join has been ignored in most of them. In this paper we design a novel algorithm called OTJFast to process ordered XML twig joins efficiently which only need to access the elements of the leaf query nodes. Experimental results show the benefits of our algorithm in term of the size of intermediate results, the number of element reads and the execution time.
15163337
A Database Encryption Scheme for Enhanced Security and Easy Sharing
Database encryption is a crucial technique in the security mechanisms of database. It is widely recognized as one of the key issues of data security. Current techniques of sharing the keys and the encrypted data for databases are neither convenient nor flexible in the real applications. Inspired by the PGP technique, we propose a novel database encryption scheme for enhanced data sharing inside a database, while preserving data privacy. It is characterized by both the fast speed of the conventional encryption and the convenience of key distribution of public key encryption. It also provides secured storage for security related data and effective key management, which enables the encrypted data to be shared conveniently. The scheme has been implemented and successfully applied on Oscar, a commercial DBMS developed by us
119485415
On efficient preconditioners for iterative solution of a Galerkin boundary element equation for the three-dimensional exterior Helmholtz problem
The paper presents a Galerkin numerical method for solving the hyper-singular boundary integral equations for the exterior Helmholtz problem in three dimensions with a Neumann's boundary condition. Previous work in the topic has often dealt with the collocation method with a piecewise constant approximation because high order collocation and Galerkin methods are not available due to the presence of a hypersingular integral operator. This paper proposes a high order Galerkin method by using singularity subtraction technique to reduce the hyper-singular operator to a weakly singular one. Moreover, we show here how to extend the previous work (J. Appl. Numer. Math. 36 (4) (2001) 475-489) on sparse preconditioners to the Galerkin case leading to fast convergence of two iterative solvers: the conjugate gradient normal method and the generalised minimal residual method. A comparison with the collocation method is also presented for the Helmholtz problem with several wavenumbers.
6083596
Improved learning algorithms for mixture of experts in multiclass classification
Mixture of experts (ME) is a modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME architecture for adjusting the parameters and the iteratively reweighted least squares (IRLS) algorithm is used to perform maximization in the inner loop [Jordan, M.I., Jacobs, R.A. (1994). Hierarchical mixture of experts and the EM algorithm, Neural Computation, 6(2), 181-214]. However, it is reported in literature that the IRLS algorithm is of instability and the ME architecture trained by the EM algorithm, where IRLS algorithm is used in the inner loop, often produces the poor performance in multiclass classification. In this paper, the reason of this instability is explored. We find out that due to an implicitly imposed incorrect assumption on parameter independence in multiclass classification, an incomplete Hessian matrix is used in that IRLS algorithm. Based on this finding, we apply the Newton-Raphson method to the inner loop of the EM algorithm in the case of multiclass classification, where the exact Hessian matrix is adopted. To tackle the expensive computation of the Hessian matrix and its inverse, we propose an approximation to the Newton-Raphson algorithm based on a so-called generalized Bernoulli density. The Newton-Raphson algorithm and its approximation have been applied to synthetic data, benchmark, and real-world multiclass classification tasks. For comparison, the IRLS algorithm and a quasi-Newton algorithm called BFGS have also been applied to the same tasks. Simulation results have shown that the use of the proposed learning algorithms avoids the instability problem and makes the ME architecture produce good performance in multiclass classification. In particular, our approximation algorithm leads to fast learning. In addition, the limitation of our approximation algorithm is also empirically investigated in this paper.
7013408
Towards better making a decision in speaker verification
Speaker verification is a process that accepts or rejects the identity claim of a speaker. How to make a decision is a critical problem; a threshold for decision-making critically determines performance of a speaker verification system. Traditional threshold estimation methods take only information conveyed by training data into consideration and, to a great extent, do not relate it to production data. It turns out that a speaker verification system with such threshold estimation suffers from poor performance in reality due to mismatches. In this paper, we propose several methods towards better decision-making in a practical speaker verification system. Our methods include the use of additional reliable statistical information for threshold estimation, elimination of abnormal data for better estimation of underlying statistics, and on-line incremental threshold update. To evaluate the performance of our methods, we have done simulations based on a baseline system, Gaussian Mixture Model, in both text-dependent and text-independent modes. Comparative results show that in contrast to the recent threshold estimation methods our methods yield considerably better performance, especially on miscellaneous mismatch conditions, in terms of generalization. Thus our methods provide a promising way for real speaker verification applications.
15908834
Ensemble Learning with Active Data Selection for Semi-Supervised Pattern Classification
Unlike traditional pattern classification, semi-supervised learning provides a novel technique to make use of both labeled and unlabeled data for improving the performance of classification. In general, there are two critical issues for semi-supervised learning of discriminative classifiers; i.e., how to create an initial classifier of a good generalization capability with the limited labeled data and the how to make an effective use of unlabeled data without degradation of the established classifier. To tackle two aforementioned problems, we propose an ensemble learning approach based on a recent active data selection strategy, where ensemble learning would yield good generalization and active data selection tends to choose the unlabeled data more likely resulting in an improvement during semi-supervised learning. By using an ensemble of K-NN classifiers, we demonstrate the effectiveness of our approach on a synthetic data classification and a facial expression recognition tasks.
5116790
Flexible parallelization of fast wavelet transforms
In this paper, we present a new parallel algorithm for fast wavelet transforms (FWT) of a matrix of arbitrary size using any given number of parallel processors. The main idea in achieving the optimal load balancing is through a complexity analysis and flops minimization. This makes parallel implementation of FWT feasible and efficient on distributed memory machines with only a small number of processors and on local area networks. The new algorithm is tested by numerical experiments.