| Publisher | Sharif University of Technology | ||
|---|---|---|---|
| Format | 178.3KB PDF | Date added | 25 Apr 2005 |
| Topics | Network Security, Security Management, Intrusion Detection Systems | ||
| Downloads | 14 | ||
Regards to the growing rate of network attacks, using intelligent methods especially neural networks in constructing efficient and reliable Intrusion Detection Systems (IDSs) to detect new unknown attacks has been the interest of some researchers in recent years. RTUNNID system; introduced in this paper; is one of such systems with capability of intelligent real-time intrusion detection using unsupervised neural networks. Ability of unsupervised nets to improve their analysis of new data over time without requirement of retraining over all the previous and new data is the main reason for selecting such nets in the IDS. In the previous work, one evaluated the Adaptive Resonance Theory (ART) and Self-Organizing Map (SOM) neural networks in intrusion detection using DARPA KDD-Cup's 99 offline data.
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