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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haddadi, F. Khanchi, S. Shetabi, M. Derhami, V. |
| Copyright Year | 2010 |
| Abstract | Fast Internet growth and increase in number of users make network security essential in recent decades. Lately one of the most hot research topics in network security is intrusion detection systems (IDSs) which try to keep security at the highest level. This paper addresses a IDS using a 2-layered feed-forward neural network. In training phase, “early stopping” strategy is used to overcome the “over-fitting” problem in neural networks. The proposed system is evaluated by DARPA dataset. The connections selected from DARPA is preprocessed and feature range is converted into [-1, 1]. These modifications affect final detection results notably. Experimental results show that the system, with simplicity in comparison with similar cases, has suitable performance with high precision. |
| Starting Page | 262 |
| Ending Page | 266 |
| File Size | 369314 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769540429 |
| e-ISBN | 9781424469628 |
| DOI | 10.1109/ICCNT.2010.28 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-04-23 |
| Publisher Place | Thailand |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Intrusion Data security Artificial neural networks Back propagation Feedforward neural networks Feed-forward Databases Neural networks Intrusion detection Information security Computer networks Internet DARPA IP networks Artificial Neural network Feedforward systems |
| Content Type | Text |
| Resource Type | Article |
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