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Due to the dynamic and anonymous nature of open environments, it is critically important for agents to identify trustful cooperators which work consistently as they claim. In the e-services and e-commerce communities, trust and reputation systems are applied broadly as one kind of decision support systems, and aim to cope with the consistency problems caused by uncertain trust relationships. However,...
Intrusion detection system (IDS) is an effective tool that can help to prevent unauthorized access to network resources. A good intrusion detection system should have higher detection rate and lower false positive. A new classification system using Jordan/Elman (J/L) neural network for ID is proposed to detect intrusions from normal connections with satisfactory detection rate and false positive....
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training...
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