Abstract
In the information age, with the increase of data feature dimensions, the performance of Intrusion Detection System (IDS) in training time and classification accuracy is declining. A large number of researchers have conducted a lot of research on how to use machine learning algorithms for fast and accurate intrusion detection. This paper presents an IDS model based on Extreme Learning Machine (ELM). Firstly, the intrusion detection data set NSL-KDD is normalized. Then, uses the normalized data as input, and the hidden layer activation function of ELM algorithm suitable for intrusion detection is optimized by comparing the correct rate of the model under different hidden layer activation functions and the number of hidden layer nodes. Finally, the optimal number of hidden layer nodes corresponding to the optimal hidden layer activation function is obtained. Experiments show that the optimized ELM has better performance.
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