Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm
This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the AB...
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| Published in | ICT express Vol. 5; no. 1; pp. 56 - 59 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier
01.03.2019
한국통신학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2405-9595 2405-9595 |
| DOI | 10.1016/j.icte.2018.01.014 |
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| Abstract | This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods. Keywords: Intrusion detection system, Cloud computing, Neural network, Artificial bee colony, Fuzzy clustering |
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| AbstractList | This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods. KCI Citation Count: 31 This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods. Keywords: Intrusion detection system, Cloud computing, Neural network, Artificial bee colony, Fuzzy clustering |
| Author | Navimipour, Nima Jafari Hajimirzaei, Bahram |
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