Beizhan Wang

Cloud computing and its key techniques

With the development of parallel computing, distributed computing, grid computing, a new computing model appeared, called cloud computing. It aims to share data, calculations, and services transparently among users of a massive grid. It became a hot issue for its advantages such as “reduce costs”, “increase business flexibility” and/or “provide business continuity”. In this paper, we described what is cloud computing and took Googles cloud computing techniques as an example, summed up key techniques, such as data storage technology (Google File System), data management technology (BigTable), as well as programming model and task scheduling model (Map-Reduce), used in cloud computing, and then some example of cloud computing vendors were illustrated and compared.

A novel mobility model based on semi-random circular movement in mobile ad hoc networks

When simulating a mobile ad hoc network (MANET), it is important to use a realistic mobility model to reflect the actual performance of a mobile system. The spatial distribution of node locations in a mobile model plays a key role when investigating the characteristics of a MANET. However, most existing mobility models with random and simple straight line movement lead to unrealistic scenarios and non-uniform distributions, and can not describe the actual movement of Unmanned Aerial Vehicles (UAVs) connected via a MANET. To address this issue, a novel mobility model based on semi-random circular movement (SRCM) is presented. The approximate node distribution function in SRCM is derived within a 2D disk region. The relationship between application performance and node distribution is investigated for a UAV MANET, with focus on scan coverage and network connectivity. A simulation using the NS2 tool is conducted. It is shown that the presented model with a uniform distribution performs better than the popular Random Waypoint mobility model. The SRCM model with the NS2 simulator provides a realistic way for simulation and performance evaluation of UAV MANETs.

Estimation of software projects effort based on function point

With the development of software industry, software estimation and measurement is catching much more concern. Scale increasing in applications and a variety of programming languages using at the same time, manual measurement based on the LOC (Line of Code) cannot meet the estimating requirements. The emergence of Function Point resolves these difficult issues. In order to obtain the software effort, we propose an estimating method for software effort based on function point. It helps to estimate software effort more accurately without considering the languages or developing environment you choose. Firstly, use actual project records to obtain the linear relation between function point and software effort. Then determine the parameters of linear equations by maximum likelihood estimating method. Finally, you can get the effort of the project according to this equation with the function point given. After obtaining the software effort, project manager can arrange the project progress, control the cost and ensure the quality more accurately.

Cloud computing applied in the mobile Internet

As a new mode of network applications, cloud computing is transforming the Internet computing infrastructure. Also the mobile applications and devices are developing rapidly. Cloud computing is anticipated to bring an innovation in the mobile Internet. In this paper, popular cloud computing is introduced, the concept and core ideas of mobile cloud computing are described, as well as its system architecture; and the existing problems and development of mobile cloud computing are summarized, and some successful applications of mobile cloud computing are discussed.

Feature selection based on Rough set and modified genetic algorithm for intrusion detection

In the Network Intrusion Detection, the large number of features increases the time and space cost, besides the irrelative redundant characteristics make the detection accuracy dropped. In order to improve detection accuracy and efficiency, a new Feature Selection method based on Rough Sets and improved Genetic Algorithms is proposed for Network Intrusion Detection. Firstly, the features are filtered by virtue of the Rough Sets theory; then in the remaining feature subset, the Optimal subset will be found out through the Genetic Algorithm improved with Population Clustering approach for the best ultimate optimized results. Finally, the effectiveness of the algorithm is tested on the classical KDD CUP 99 data sets, using the SVM classifier for performance evaluation. The experiment shows that the new method improves the accuracy and efficiency in Network Intrusion Detection compared with the related researches of the intrusion detection system.

Energy-aware and self-adaptive anomaly detection scheme based on network tomography in mobile ad hoc networks

Anomaly detection is indispensable for satisfying security services in mobile ad hoc network (MANET) applications. Often, however, a highly secure mechanism consumes a large amount of network resources, resulting in network performance degradation. To shift intrusion detection from existing security-centric design approaches to network performance centric design schemes, this paper presents a framework for designing an energy-aware and self-adaptive anomaly detection scheme for resource constrained MANETs. The scheme uses network tomography, a new technique for studying internal link performance based solely on end-to-end measurements. With the support of a module comprising a novel spatial-time model to identify the MANET topology, an energy-aware algorithm to sponsor system service, a method based on the expectation maximum to infer delay distribution, and a Self-organizing Map (SOM) neural network solution to profile link activity, the proposed system is capable of detecting link anomalies and localizing malicious nodes. Consequently, the proposed scheme offers a trade-off between overall network security and network performance, without causing any heavy network overload. Moreover, it provides an additional approach to monitor the spatial-time behavior of MANETs, including network topology, link performance and network security. The effectiveness of the proposed schemes is verified through extensive experiments.

Survey on HMM based anomaly intrusion detection using system calls

Intrusion detection based on System calls is a very important domain of anomaly detection, this paper firstly introduces the basic idea of Hidden Markov Model (HMM) based intrusion detection, and then expounds its current development with comparisons about the merits and shortcomings to the current mainstream technologies, by the way, presenting some further discussions upon the future direction of the HMM-based intrusion detection.

3D vasculature segmentation using localized hybrid level-set method

BackgroundIntensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image.MethodsThis paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images.ResultsExperiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model.ConclusionsExperimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.

A method for HMM-based system calls intrusion detection based on hybrid training algorithm

HMM (Hidden Markov Model) is a very important intrusion detection tool. The classical HMM training algorithm is a climbing algorithm. It can only find a local optimal solution. To improve the accuracy of HMM training, this paper introduces a hybrid algorithm into intrusion detection. Experiments show that this algorithm can find a more accurate model.

Analysis and solution of data quality in data warehouse of Chinese materia medica

The data quality problem often be ignored in the process of data warehouse construction and utilization. In order to avoid the phenomenon of “garbage in, garbage out” which will influence the decision-making, the problem of data quality must be paid great attention. In this paper, we took the Chinese materia medica (Cmm) data warehouse in China Academy of Chinese Medical Sciences (CACMS) as example. We analyzed the data quality problems in the process of its construction, cited the reasons for bad data quality, and gave the key elements and their relations for data quality analysis and assessment. At the end of article, we proposed a series of assessment standards and solution scheme to improve the data quality in Cmm data warehouse, and carried out in practice to prove it.