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A computer network intrusion detection and prevention system consists of collecting network traffic data, discovering user behavior patterns as intrusion detection rules, and applying these rules to prevent malicious and misuse. Many commercial off-the-shelf (COTS) products have been developed to perform each of these tasks. In this paper, the component-based software engineering approach is exploited...
Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Some commercial tools for collecting network traffic data exist, such as SNORT. The traffic data collected from the network using these tools, however, usually doesnpsilat fit the format requirement of the...
In this paper, we discuss our research in developing general and systematic methods for intrusion prevention. The key idea is to use data mining techniques to discover regular patterns of system features that describe program and user behavior. Server systems invariably write detailed activity logs whose value is useful in detecting intrusion. Unfortunately, production volumes overwhelm the capacity...
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