During the distributing and downloading of Android application packages, it is always be vulnerable to download hijacking attacks. Traffic analysis could be used by sites to detect if they are under this kind of regular download hijacking attacks. But the stealth download hijacking attacks cannot be discovered by using such a method. Based on the discovery and analysis of an actual case, a vulnerability of Android application package stealth download hijacking was proposed. Attackers exploited this vulnerability to implement a stealth download hijacking through deploying bypass devices between the downloaders and the publishers. And the victim sites can hardly notice it by using current methods. The cause, influence and mechanism of the vulnerability were discussed, and a solution was tried to put forward in respects of distributed detection, centralized analysis and active prevention.

The inconformity of user evaluation standard and preference results in unfair comparability between online services in cyberspace, thereby the users are hardly to choose satisfactory online services. The ranking method to calculate the online service quality based on social choice theory was proposed. First, group preference matrix was built according to the user-service evaluation matrix given by users; second, 0-1 integer programming model was built based on group preference matrix and Kemeny social choice function; at last, the optimal service ranking results could be obtained by solving this model. The individual preferences were aggregated to group preference in the proposed method; the decision was consistent with the majority preference of the group and maximum consistency with the individual preference. The proposed method's rationality and effectiveness were verified by theoretical analysis and experiment results. The experimental results show that the proposed method can solve the incomparability between online services, realize the online service quality ranking, effectively resisted the recommendation attacks. So it has strong anti-manipulation.

Large amount of user rating data, involving plentiful users' opinion and preference, is produced in e-commerce applications. An construction and inference method for latent variable model (i.e., Bayesian Network with a latent variable) oriented to user preference discovery from rating data was proposed to accurately infer user preference. First, the unobserved values in the rating data were filled by Biased Matrix Factorization (BMF) model to address the sparseness problem of rating data. Second, latent variable was used to represent user preference, and the construction of latent variable model based on Mutual Information (MI), maximal semi-clique and Expectation Maximization (EM) was given. Finally, an Gibbs sampling based algorithm for probabilistic inference of the latent variable model and the user preference discovery was given. The experimental results demonstrate that, compared with collaborative filtering, the latent variable model is more efficient for describing the dependence relationships and the corresponding uncertainties of related attributes among rating data, which can more accurately infer the user preference.

To solve the problem of large amount of calculation and nonlinear programming in the process of service composition optimization, a Cost Benefit Coefficient (CBC) approach was proposed for Web services composition reliability optimization in the situation of a given cost investment. First, the structure patterns of service composition and related reliability function were analyzed. Furthermore, the Web service composition method of reliability calculation was proposed and a nonlinear optimization model was established accordingly. And then the cost benefit coefficient was computed through the relationship between the cost and the reliability of component services, and the optimization schemes of Web service composition were decided. According to the nonlinear optimization model, the results of optimization were computed. Finally, given cost investment, the higher reliability of the approach to optimize the reliability of Web service composition was verified through the comparison of this approach and the traditional method on the reliable data of component service. The experimental results show that the proposed algorithm is effective and reasonable for reliability optimization of Web services composition.

To distribute the score of composite service obtained from customer to each component service based on actual and historical performance of component services, Analytic Hierarchy Process (AHP) was used to calculate the distribution weight of each component service, in which a method was presented to convert Web service process into structure tree process, and the weight matrix was used to calculate the weight of each node in the tree structure. The relationship between actual Quality of Service (QoS) of component services and its advertised utility interval of QoS were taken into consideration, and through deviation function, the deviation proportion between actual QoS utility value of component service and actual QoS average utility value of all component services was calculated, meanwhile the influence on score distribution by history performance of each component service was considered. The experimental results show that actual QoS and history performance of component services have some influence on score which was distributed, and demonstrate that the proposed approach can achieve a reasonable and fair score distribution.

In response to the issue that the intervertebral disk manual modeling was time-consuming and subjective, and the existing segmentation method was not accurate enough, a new method named two-diememsional Automatic Active Shape Model (2D-AASM) was proposed. It included three parts: automatic statistical shape modeling of intervertebral disk based on minimum description length, 2D local gradient modeling and segmentation. Adopting the manual segmentation results of 25 sets of spinal MR images as the training set, the study used minimum description length method to determine the point correspondence, built statistical shape model and 2D local gradient model for intervertebral disk T4-5. The generated shape model had lower variance and the objective function value than the manual and arc length parameter method. After the model was built, three sets of Magnetic Resonance Image (MRI) images were used to test the proposed method. Compared with the traditional ASM and 1D-ASM, the segmentation result of the proposed method had a higher Dice coefficient and lower over-segmentation and under-segmentation rate. The experiment results indicate that the proposed method generates a better model and more accurate segmentation result.

For various applications in today’s service-oriented enterprise computing systems, such as process-oriented service discovering or clustering, it is necessary to measure the distance between two process models. In this paper, we propose a quantitative measure to calculate the distance or similarity between different structured processes. We first introduce a structured workflow model and transform each process into a process structure tree, and then calculate the process distance and its similarity based on the tree edit distance of two structure trees. The proposed distance metric satisfies three distance measure properties, i.e., identity of indiscernible, symmetry and triangle inequality. These properties make the distance metric can be used as a quantitative tool in effective process model management activities. Experiment studies show that the method is feasible. Compared to the adjacency matrix method, the proposed method is more reasonable due to the semantic distance between different structures is considered.

To manage the risk of service, it is necessary to obtain stochastic character of Quality of Service (QoS) that is represented as accurate probability distribution. This paper presented an approach to estimate probability distribution of Web service QoS in the case of small number of samples. Using max entropy principle, the analytical formula of the probability density function can be obtained by transforming the probability distribution estimation problem into an optimal problem with constraints obtained from sampling QoS data. Then an algorithm to estimate parameters of the probability density function was designed. The experimental and simulation results based on real Web service QoS data show the effectiveness of the proposed approach for probability distribution estimation of different QoS attribute. The efficiency and feasibility of the distribution estimation algorithm have got validated by experiments too.

In order to reduce the outage probability and enhance the stability of cognitive system, an improved algorithm of spectrum allocation based on classical graph coloring model was proposed. A difference factor of spectrum's idle time and user's request time was introduced. For every cognitive user, the algorithm allocated spectrums according to two factors: the spectrum efficiency and the time difference factor. Cognitive user with greater product value of the two factors was prior. The simulation results show that the outage probability of improved algorithm is far below that of the previous algorithm.