| Publisher | Association for Computing Machinery | ||
|---|---|---|---|
| Format | 134.2KB PDF | Date added | 27 Feb 2002 |
| Topics | Network Security, Security Management, Intrusion Detection Systems | ||
| Downloads | 15 | ||
Traditional Network Intrusion Detection Systems (NIDSs) use rules to detect intrusions, with these rules being updated manually by knowledgeable engineers. With today's complex network environment, a new systematic method is desired to detect intrusions automatically. Data mining techniques can be used to add a systematic intrusion detection capability to NIDSs. Data Mining is concerned with uncovering patterns, associations, changes, anomalies, and statistically significant structures and events in data. This paper describes a Neural Network (NN) based NIDS architecture. The paper introduces the experiment on the data available from the 1998 DARPA Intrusion Detection Evaluation. The paper also presents a real-time implementation of a NN based NIDS for Denial of Service (DoS) attacks using open source software.
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