| Publisher | New Mexico Tech | ||
|---|---|---|---|
| Format | 91.2KB PDF | Date added | 18 Jan 2006 |
| Topics | Artificial Intelligence, Network Security, Intrusion Detection Systems | ||
| Downloads | 0 | ||
Computational Intelligence (CI) methods are increasingly being used for problem solving. This paper concerns using CI-type learning machines for intrusion detection, which is a problem of general interest to transportation infrastructure protection since a necessary task thereof is to protect the computers responsible for the infrastructure's operational control, and an effective Intrusion Detection System (IDS) is essential for ensuring network security. Two classes of learning machines for IDSs are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). This paper shows that SVMs are superior to ANNs in three critical respects of IDSs: SVMs train and run an order of magnitude faster; SVMs scale much better; and SVMs give higher classification accuracy.
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