3094023
Fuzzy Classifier with Probabilistic IF-THEN Rules
The typical fuzzy classifier consists of rules each one describing one of the classes. This paper presents a new fuzzy classifier with probabilistic IF-THEN rules. A learning algorithm based on the gradient descent method is proposed to identify the probabilistic IF-THEN rules from the training data set. This new fuzzy classifier is finally applied to the well-known Wisconsin breast cancer classification problem, and a compact, interpretable and accurate probabilistic IF-THEN rule base is achieved.
26786201
Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating
This paper presents a theory called conditional Dempster-Shafer theory (CDS) for uncertain knowledge updating. In this theory, a prioriknowledge about the value attained by an uncertain variable is modeled by a fuzzy measure and the evidence about the underlying uncertain variable is modeled by the Dempster-Shafer belief measure. Two operations in CDS are discussed in this paper, the conditioned combination rule and conditioning rule, which deal with evidence combining and knowledge updating, respectively. We show that conditioned combination rule and conditioning rule in CDS satisfy the property of Bayesian parallel combination.
30023273
MedTextus: an intelligent web-based medical meta-search system
We propose to demonstrate a web-based prototype system that integrates the meta-search approach with existing information analysis and visualization technologies to facilitate concept-based searching behavior over the medical domain. The system distinguishes itself from other meta-search engines through two features. It incorporates the co-occurrence analysis and existing ontology to understand user's query. It also utilizes the self-organizing map (SOM) to categorize and visualize search results.
46340869
Study on Multidisciplinary Collaborative Design Based on Regression Analysis
12614266
SVision: A Network Host-Centered Anomaly Visualization Technique
We proposed a technique merged from a combination of both anomaly and graphical methods, for intrusion detection. The network is pictured as a community of hosts that exchange messages among themselves. Our aim is to graphically highlight those hosts that represent a possible threat for the network, so that a network administrator will be able to further explore the anomaly and decide upon the responses that are appropriate. We choose to test our view against the DARPA 99 intrusion detection and evaluation dataset since it provides labels which we can use to monitor our system. Experiments show our visualization technique as a possible alternative for detection of network intrusions, in particular Denial of Service (DoS) and Distributed-DoS attacks such as Ping Of Death, UDP storm, SSH Process Table, and Smurf, to name a few.
4515160
Alert Correlation for Extracting Attack Strategies
Alert correlation is an important technique for managing large the volume of intrusion alerts that are raised by heterogenous Intrusion Detection Systems (IDSs). The recent trend of research in this area is towards extracting attack strategies from raw intrusion alerts. It is generally believed that pure intrusion detection no longer can satisfy the security needs of organizations. Intrusion response and prevention are now becoming crucially important for protecting the network and minimizing damage. Knowing the real security situation of a network and the strategies used by the attackers enables network administrators to launches appropriate response to stop attacks and prevent them from escalating. This is also the primary goal of using alert correlation technique. However, most of the current alert correlation techniques only focus on clustering inter-connected alerts into different groups without further analyzing the strategies of the attackers. Some techniques for extracting attack strategies have been proposed in recent years, but they normally require defining a larger number of rules. This paper focuses on developing a new alert correlation technique that can help to automatically extract attack strategies from a large volume of intrusion alerts, without specific prior knowledge about these alerts. The proposed approach is based on two different neural network approaches, namely, Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The probabilistic output of these two methods is used to determine with which previous alerts this current alert should be correlated. This suggests the causal relationship of two alerts, which is helpful for constructing attack scenarios. One of the distinguishing feature of the proposed technique is that an Alert Correlation Matrix (ACM) is used to store correlation strengthes of any two types of alerts. ACM is updated in the training process, and the information (correlation strength) is then used for extracting high level attack strategies.
19034041
Study on Agent-Based Parts Resource Application System for Commercial Design
An effective way of retrieving components is needed in order to make the implementation of product commercial development activity. In this paper, the components resource application method based on the software agent is proposed first. Then, on the base of comparison to the product commercial design components resources application system and the traditional supply chain system, three kind of application patterns based on the present network components resources are analyzed: the on-line purchase pattern, the on-line inquiry pattern and the on-line assign pattern. Then, according to the commercial development characteristic, the concrete structure of software agent suit to the commercialization performance is given. Corresponding, the components coding agent, the solid - commercial mapping and optimizing agent as well as managing agent are designed. From the initial period geometrical modeling, the intermediate stage commercial design as well as the entire process design management, the analysis of these three aspects have been carried on separately. In the end, the research work indicated that, the agent technology is one effective application method used to realizing the magnanimous components data analysis over the network