A new intrusion detection system based on fast learning network and particle swarm optimization
Supervised Intrusion Detection System is a system that has the capability of learning from examples about previous attacks to detect new attacks. Using ANN based intrusion detection is promising for reducing the number of false negative or false positives because ANN has the capability of learning f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/22096/ http://umpir.ump.edu.my/id/eprint/22096/ http://umpir.ump.edu.my/id/eprint/22096/ http://umpir.ump.edu.my/id/eprint/22096/1/08326489.pdf |
| Summary: | Supervised Intrusion Detection System is a system that has the capability of learning from examples about previous attacks to detect new attacks. Using ANN based intrusion detection is promising for reducing the number of false negative or false positives because ANN has the capability of learning from actual examples. In this article, a developed learning model for Fast Learning Network (FLN) based on particle swarm optimization(PSO) has been proposed and named as PSO-FLN. The model has been applied to the problem of intrusion detection and validated based on the famous dataset KDD99. Our developed model has been compared against a wide range of meta-heuristic algorithms for training ELM, and FLN classifier. PSO-FLN has outperformed other learning approaches in the testing accuracy of the learning. |
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