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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kachurka, P. Golovko, V. |
| Copyright Year | 2011 |
| Description | Author affiliation: Brest State Technical University (Kachurka, P.; Golovko, V.) |
| Abstract | Modern intrusion detection systems process large amounts of data. Most systems use signature- and rule-based approaches to find attack traces. The main disadvantage of such technologies is the need of continuous updating of signature database to let the system detect newest attacks. We present recirculation neural network based approach which lets to detect previously unseen attack types in real-time mode and to further correct recognition of this types. The experiments held on both KDD data and real network traffic data prove that this approach can be used in host-based anomaly and misuse detectors. |
| Starting Page | 393 |
| Ending Page | 397 |
| File Size | 136572 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781457714269 |
| e-ISBN | 9781457714252 |
| DOI | 10.1109/IDAACS.2011.6072781 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-09-15 |
| Publisher Place | Czech Republic |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Detectors Intrusion detection Training Image reconstruction Artificial neural networks Real time systems artificial neural networks Intrsuion detection classification |
| Content Type | Text |
| Resource Type | Article |
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