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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | PingZhao Hu Heywood, M.I. |
| Copyright Year | 2003 |
| Description | Author affiliation: Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada (PingZhao Hu; Heywood, M.I.) |
| Abstract | Intrusion Detection Systems are typically deployed for real time operation, but are limited to identifying attacks once initiated. In this work we instead investigate the potential for predicting an attack before it occurs. To do so, a two-stage process is employed with a classification stage following that of a predictor. Predictors are based on the SOM and classifier on an SVM. Training and test is conducted using the 'TCP' connection features from the DARPA KDD competition data set. In spite of the simplicity of the model, the system is able to provide false positive and false negative rates of 23.8% and 7.1% respectively for one step-ahead prediction. |
| Sponsorship | Int. Neural Network Soc. IEEE Neural Networks Soc |
| Starting Page | 1780 |
| Ending Page | 1785 |
| File Size | 438745 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780378989 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2003.1223677 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-07-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Predictive models Support vector machines Support vector machine classification Intrusion detection Testing Organizing Computer science Real time systems Pattern analysis Statistical analysis |
| Content Type | Text |
| Resource Type | Article |
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