Abstract
Network intrusion detection system provides a better network security solution than other traditional network defense technologies. Aiming at the increasingly serious problem of Internet security in the big data environment, a network intrusion detection model based on autoencoder network model and improved genetic algorithm BP (IGA‐BP) network is constructed. In order to reduce the data dimension and eliminate redundant information, the autoencoder network model is firstly used to denoise and dedimension. A new population was formed by selecting some of the best parent individuals for cross mutation and replacing the worst parent individuals. The improved genetic algorithm and new population generation model will provide more reasonable initial parameters for BP network, namely, IGA‐BP network model. Based on IGA‐BP network model, the problems of slow detection rate and easy to get into local optimality in BP network are solved. The experiments were performed on KDD CUP99 dataset, which simulated different types of user organizations and different types of network intrusion. Compared with the existing intrusion detection methods, the experimental results show that the proposed method has a great effect on classification accuracy, false positives, and detection rate.
Highlights
With the rapid development of network and its wide application in various fields, the situation of network security is becoming more and more serious
This paper proposed a network intrusion detection algorithm based on autoencoder network model and improved genetic algorithm BP (IGA-BP) model
The population generation algorithm of GA model is improved, and the improved genetic algorithm which improves the generation of new population will provide more reasonable initial parameters for BP network
Summary
With the rapid development of network and its wide application in various fields, the situation of network security is becoming more and more serious. Principal component analysis (PCA) was used to reduce the dimension of input features, and artificial neural network (ANN) was used as the classification model. Paper [7] proposed a hybrid dimension reduction intrusion detection method based on information gain (IG) and principal component analysis (PCA). Paper [13] proposed a new map reduce method for data mining and pattern recognition in the big data environment This algorithm can determine the minimum reduction rough set and realize the parallel genetic algorithm. Due to the lack of effective reduction of redundant attributes, the detection rate and even the correctness of detection will be reduced when the big data of network intrusion is analyzed.
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