Computer network

Mathematics

Intrusion detection system

Statistic

Operating system

Algorithm

Data mining

Statistics

Mean squared error

Network packet

Artificial intelligence

Multilayer perceptron

CloudSim

Computer science

Cluster analysis

Cloud computing

Artificial bee colony algorithm

Artificial neural network

ICT Express

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.

Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm