| 摘要 |
An intrusion detection system (IDS) is enhanced to operate in a cluster of such systems, and IDSs organized into a cluster cooperate to exchange IP reputation influencing events information between or among the cooperating systems in real-time to enhance overall system response time and to prevent otherwise hidden attacks from damaging network resources. An IDS includes an IP reputation analytics engine to analyze new and existing events, correlate information, and to raise potential alerts. The IP reputation analytics engines may implement an algorithm, such as a pattern matching algorithm, a continuous data mining algorithm, or the like, to facilitate this operation. Clustering IDS endpoints to share IP reputation influencing events, using the cluster-wide view to determine IP reputation, and feeding the cluster-wide view back to the IDS endpoints, provides for enhanced and early detection of threats that is much more reliable and scalable as compared to prior art techniques. |
