Applied Mechanics and Materials Vols. 556-562

Abstract: The method of anomaly detection in traditional software system cannot locate anomaly or find the lack of abnormal source accurately and timely. With regard to this deficiency, this paper presents an improved algorithm based on biological immune dendritic cell algorithm. This method aims to modify PAMP signal to achieve the purpose of locating anomaly source. It proves not only applicable to the real-time detection, but also to locate the anomaly source and processing, which further improves the accuracy of anomaly detection.

Abstract: Aiming at the security issues of cloud computing data center, the systematic security construction architecture of cloud computing data center is proposed. By surrounding the key aspects of security construction, such as infrastructure security, virtual security, cloud authentication and authorization, data isolation and protection, cloud platform and cloud service security, security operation maintenance, cloud computing platform migration, and disaster recovery backup, the security architecture constructs a multi-level, multi-angle tridimensional defense system in depth. It ensures the life cycle security for resource services of the cloud computing data center. Many key problems are further discussed in detail, such as the problem of data storage security, security domain isolation, cloud computing platform tenants accessing, and terminal accessing. This paper provides reference for the security construction of cloud computing data center, and gives guide to the implementation of the relevant security measures.

Abstract: Cloud computing is developing rapidly in recent years. Everyone is talking about cloud computing that provide large scale service to replace computers and software. Cloud computing has already become the development trend of present IT circles. This paper explains the basics of Cloud computing, analyzes the difference of cloud computing services, and points out the characteristics of the cloud computing services.

Abstract: The text message which has been viewed and edited is stored in physical memory. In this paper, we take the notepad process as sample, designs a recovery scheme for user’s viewing-data and judge whether the document was edited, First this scheme extracts the data of all notepad’s process in memory with the member information of the process’s EPROCESS structure, and then, matches the data with the target string, thus, it can recovery the viewing-data of a different order. Experiments show that this scheme can recovery the text message when the user browsing in the last minutes and analyze the behavior of the user.

Abstract: In response to store and retrieve massive Data, many companies have prefer using a distributed database. HBase, as an open source distribute column-oriented database, has been widely used for its advantages of low cost and high scalability. As an important basis of retrieving stored data, the design of rowkey will directly affect the efficiency and success rate of retrieving data in HBase database. By classifying information and encoding the data type in rowkey design by Huffman coding, data type can be effectively bound with the data content in order to provide the efficient solution for class-based information retrieving. The results of experiment shows that this design achieves retrieving information by category as well as improve the overall efficiency of retrieval.

Abstract: As the cloud computing is becoming increasingly popular, more enterprise and individuals tend to use cloud to store data. As a convenient way of data storage, however, the network security became the greatest concern to all kinds of users. In terms of the security problem, three different approaches are introduced to deal with this problem in this paper. And the performance, load and security of these data access control methods will be discussed in detailed.

Abstract: Ontology matching is the task of finding alignments between two different ontologies. It has become the key point of building knowledge base and integrating heterogeneous data. In this paper, a novel ontology matching approach that is based on continual word embedding is proposed. We describe in details how is skip-gram model adapted to capture the semantic of words to learn the word embedding. After computing the name similarity of concepts, similarity flooding algorithm is used to fix the initial similarity. Experiments on Ontology Alignment Evaluation Initiative (OAEI) benchmark without instances show that the proposed method significantly improves the quality of mappings.

Abstract: The Internet has experienced profound changes. Large amount of user-generated-contents provide valuable information to the public. Customers usually express their opinion in online shopping. After they finish the reviews, they give an overall rating to the product or service. In this paper, we focus on the review rating prediction problem. Previous studies usually regard this problem as a regression problem. We take a new machine learning method to solve the problem. Learning to rank method has been exploited to tackle the prediction. After feature selection, the maximum entropy classifier has been employed to solve the multi-classification problem. The real life dataset has been crawled to verify the proposed method. Empirical studies demonstrate the proposed method outperform the baseline methods.

Abstract: With the development of society and the progress of technology, the Internet has become an important tool for human to access information. The Internet brings information to the society and greatly facilitates information sharing, but it also brings a lot of security risks. The proportion of cross-site scripting attacks topped the list of web vulnerabilities, so this paper proposes the technology which is called mining techniques of XSS vulnerabilities based on web crawler. It includes web crawler module, code injection module and vulnerability detection module. The experiment results indicate that it has better detection.

Abstract: Network Security Situation Awareness (NSSA) is a hot topic in network security field, and cloud computing is a new technology integrated virtual storage and distributed computing. It has become the challenging questions how to provide efficient and reliable service for NSSA based on the cloud computing.This paper proposes a cloud security situation awareness model based on data mining, and puts forwarda parallelfrequent-tree Apriori algorithm (PFT-Apriori) for mining association rules. Compare with the traditional Apriori algorithm, the experimental results show that the performance of system is increased by 51% under PFT-algorithm.