Intrusion Detection System Based on Neural Networks Using Bipolar Input with Bipolar Sigmoid Activation Function
Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are used as another wall to protect computer systems and to identify corresponding vulnerabilities. The purpose of this paper is to use Backpropagation algorithm for IDS by applying bipo...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Mosul University
2011-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.mosuljournals.com/article_163644_a9bd6deea1d616fc8e682b3a54eba967.pdf |
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| Summary: | Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are used as another wall to protect computer systems and to identify corresponding vulnerabilities. The purpose of this paper is to use Backpropagation algorithm for IDS by applying bipolar input “input is represented as (1, -1)”, and bipolar sigmoid activation function.
The KDD Cup 99 dataset is used in this paper. Number of train dataset is 4947 connection records, and number of test dataset is 3117 connection records. The results of the proposed method show that the PSP is 88.32 and CPT equal to 0.286. |
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| ISSN: | 1815-4816 2311-7990 |