Applied Mechanics and Materials Vols. 380-384

Abstract: Cater to the needs of company, we have developed an access check system based on multilevel firewalls which can effectively control the clients' access to the server. Its work flow consists of four steps, namely centralized administration, filling in applications, examining applications and automatically altering firewalls. This system can promote the company's efficiency to the maximum by using minimum human resources.

Abstract: Reverse skyline is useful for supporting many applications, such as marketing decision,environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a needfor processing probabilistic reverse skyline queries. In this paper, we study the problem of efficientlyprocessing these queries on uncertain data streams. We first show the formal definitions of reverseskyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRSto maintain the most recent N uncertain data elements and to process continuous queries on them.CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structurenamed Characteristic Rectangle, are incorporated into it to handling the extra computing complexityarising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniquesare very efficient in handling uncertain data streams.

Abstract: In the process of network intrusion detection, the network operating data need to be counted. Then, the network intrusion detection can be performed through comparing the values of the statistical results with the threshold values of network intrusion detection sequentially. However, too large network operating data will cause the overlapping of operating data during the detection, reducing the accuracy of the network intrusion detection. In order to avoid the defect mentioned above, a large data network intrusion detection algorithm introduced with quantum optimization neural network is proposed. Through the analysis of the principal component of the data, the process of the massive network operating data can be simplified. Using the quantum neural network method, the initial threshold of network intrusion feature can be achieved, so as to provide accurate data base for the network intrusion detection. Taking the advantage of small distance parade of genetic algorithms, the threshold characteristic is optimized and the mass redundancy interference characteristic is overcome, so as to fulfill the network intrusion detection. Experimental results show that the proposed algorithm used for network intrusion detection can improve the accuracy of detection effectively and achieve satisfactory results.

Abstract: The detection of elements in top part of research paper is very important, because these elements are often used as the search items by user. This paper provides a mixed method for auto detection of top part from research paper. The papers feature of keyword, layout and content similarity are mixed to accurately find the area of top part and recognize the elements in top part. Experiments show the advantage of our method over existing methods, and future work is also described in the paper.

Abstract: For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.

Abstract: nteroperable role-based access control model (IRBAC 2000) has solved inter-domains authentication access through role-mapping, but it may appear related conflicts in the process of role-mapping and then posing a security risk. This study introduces the conflict detection mechanism on the inter-domain of Role-Mapping process, and it is effectively and quickly detected the associated conflicts, and gives the detection algorithm and experimental verification, to improve the inter-domain security interoperable.

Abstract: Our understanding for the traffic behaviors on P2P networks is, unfortunately, still incomplete in the sense that the current studies on traffic measurement primarily focus on the traffic in large, and insufficient efforts have been devoted to the investigation of the inter-peer traffic. Toward resolving this issue, we in this paper propose and implement a method for measuring the traffic between any individual peers. Through deploying this measurement method on the PlanetLab platform, traffic data are collected and analyzed in the following aspects: data correctness, Poisson property, traffic self-similarity of individual peers, and traffic self-similarity of the BT system. Results of these analyses suggest that the proposed method is feasible, effective, and can be extended to larger networks.

Abstract: In order to solve the problems of high false alarm rate and fail rate in intrusion detection system of Computer Integrated Process System (CIPS) network, this paper takes advantage that Genetic Algorithm (GA) possesses overall optimization seeking ability and neural network has formidable approaching ability to the non-linear mapping to propose an intrusion detection model based on Genetic Algorithm Neural Network (GANN) with self-learning and adaptive capacity, which includes data collection module, data preprocessing module, neural network analysis module and intrusion alarm module. To overcome the shortcomings that GA is easy to fall into the extreme value and searches slowly, it improves the adjusting method of GANN fitness value and optimizes the parameter settings of GA. The improved GA is used to optimize BP neural network. Simulation results show that the model makes the detection rate of the system enhance to 97.11%.

Abstract: At present, deep web information mining is a considerably potential research area. How to get this massive and valuable information hidden after the database is need to be studied further. So this paper presents an approach that includes web pages analysis, getting forms, form analysis, automatic form filling, automatic form submission and acquiring returning pages. The aim is to let the computer automatically complete this process. The experimental results show the feasibility of this method. It can automatically complete the entire process.

Abstract: Traffic attack and false data aggregation attack are serious to wireless sensor networks. How to detect the two kinds of attack is a difficult problem. An energy-efficient attack detection protocol is proposed in this paper. The detection protocol uses linear prediction to establish easy ARMA(2,1) model for sensor nodes. In the detection protocol, different nodes play different roles, and use different monitor schemes. Virtual cluster head and monitor nodes are elected. Monitor nodes monitor cluster head, and member nodes are monitored by their cluster head. At the same time, secure data aggregation schemes are added to the protocol. Simulation shows that, the detection protocol can real-time predict traffic attacks, and insure correct data aggregation, but consume less energy.