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Anomaly detection technique play an extraordinary role in the Intrusion Detection System (IDS) for its ability to detect novel attacks. To overcome the high-dimensionality problem the anomaly detection cursed of, we propose a novel Meta-Heuristic-based Sequential Forward Selection (MH_SFS) feature selection algorithm, which can be generally implemented in anomaly detection system. It is an improvement...
This paper reports a hypergraph model for online social networks with an emphasis on the node preference. Some improvements of the model are made in the present study. First, the inherent nodes properties and their links are utilized in the proposed evaluation model. Second, the proposed model contains a topology potential value of node, which is based on cognitive data field in physics. In the calculation...
Gene selection plays a crucial role in the analysis of microarray data with high dimensionality and small sample size. Incremental wrapper based feature subset selection (FSS) methods, among various feature selection approaches, tend to obtain high quality feature subset and better classification accuracy than filter methods, while it is much more time consuming since the interdependence and redundancy...
Data anonymization techniques are the main way to achieve privacy protection, and as a classical anonymity model, K-anonymity is the most effective and frequently-used. But the majority of K-anonymity algorithms can hardly balance the data quality and efficiency, and ignore the privacy of the data to improve the data quality. To solve the problems above, by introducing the concept of “diameter” and...
Emptiness checking is a key operation in the automata-theoretic model checking approach to LTL verification. Explicit state model checkers typically construct the automata on-the-fly and explore their states using depth-first search (DFS). We first cover the fundamentals of emptiness checking and summarize two important emptiness checking theorems for deciding the product automata nonempty. We then...
As a new branch of data mining, privacy preserving data mining has become more and more important in the information security field. This paper first presents an insight into the principles of privacy preserving data mining, and then marks out the difference with normal data mining through three stages, including single data record methods, centralized dataset mining technology and secure multiparty...
With the rising of data mining technology and the appearances of data stream and uncertain data technology etc, individual data, the enterprise data are possibly leaked at any moments, so the data security has become nowadays the main topic of information security. The common way to protect privacy is to use K-anonymity in data publishing. This paper will analyse comprehensively the current research...
Normal SVM is not suitable for classification problems on large data sets because of high training complexity. To build a distributed learning framework and apply cooperative learning strategy with multiple SVM classifiers are the good inspirations to data stream mining. In this paper, a SVMs' cooperative learning strategy based on multiple agent system is proposed according to cooperative and distributional...
Based on the theories of the intrusion trapping and natural language understanding, oriented e-government affairs security issues, this paper proposed a content-based self-feedback model at the point of attackers. By this model, the concrete information under attacking can be focused and the attack methods would be ignored in a standard honey trap. With the supporting of honey nets, The target sensitivity...
Control chart is one of important tools for on-line quality control. It is most difficult to identify unnatural patterns which are associated with a specific set of assignable causes on quality control charts. This paper discusses about control charts patterns recognition, and proposes a method for feature extraction from control chart based on principal component analysis(PCA). First, the principal...
Agriculture is the basic industry of the national economy, and agriculture is a so important industry especially for China. Therefore, the healthy development of agriculture and rural economy affects the overall situation of our national economy. It has important practical significance to study modern logistics system in rural areas. The core of fuzzy comprehensive evaluation on modern rural logistics...
The core of fuzzy comprehensive evaluation is membership degree transformation. But the existing transformation methods should be questioned, because redundant data in index membership degree is also used to compute object membership degree, which is not useful for object classification. The paper applied an improved algorithm for membership degree transformation in fuzzy evaluation on urban green...
Agriculture is the basic industry of the national economy, and agriculture is a so important industry especially for China. Therefore, the healthy development of agriculture and rural economy affects the overall situation of our national economy. It has important practical significance to study modern logistics system in rural areas. The core of fuzzy comprehensive evaluation on modern rural logistics...
Branch and bound for semi-supervised support vector machines as an exact, globally optimal solution is useful for benchmarking different practical S3VM implementation. But, global optimization can be computationally very demanding. Parallel implementation of the algorithm enables us to reduce computational time significantly and to solve larger problems. Focusing on the time consuming problem of BBS3VM,...
Support vector machine (SVM) has become a popular classification tool but one of its disadvantages is large memory requirement and computation time when dealing with large datasets. Parallel methods have been proposed to speed up the process of training SVM. An improved cascade SVM training algorithm is proposed, in which multiple SVM classifiers are applied. The support vectors are obtained by feeding...
Support vector machine (SVM) is originally developed for binary classification problems. In order to solve practical multi-class problems, various approaches such as one-against-rest (1-a-r), one-against-one (1-a-1) and decision trees based SVM have been presented. The disadvantages of the existing methods of SVM multi-class classification are analyzed and compared in this paper, such as 1-a-r is...
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