Aiming at the inherent problems of swarm intelligence algorithm, such as falling into local extremum in early stage and low precision in later stage, this paper proposes an improved sparrow search algorithm (ISSA). Firstly, we introduce the idea of flight behavior in the bird swarm algorithm into SSA to keep the diversity of the population and reduce the probability of falling into local optimum; Secondly, we creatively introduce the idea of crossover and mutation in genetic algorithm into SSA to get better next-generation population. These two improvements not only keep the diversity of the population at all times but also make up for the defect that the sparrow search algorithm is easy to fall into local optimum at the end of the iteration. The optimization ability of the improved SSA is greatly improved.
Extreme learning machine (ELM) is a novel single-hidden layer feedforward neural network to obtain fast learning speed by randomly initializing weights and deviations. Due to its extremely fast learning speed, it has been widely used in training of massive data in recent years. In order to adapt to the real network environment, based on the ELM, we propose an improved particle swarm optimized online regularized extreme learning machine (IPSO-IRELM) intrusion detection algorithm model. First, the model replaces the traditional batch learning with sequential learning by dynamically adapting the new data obtained in the training network instead of training all collected samples in an offline manner; second, we improve the particle swarm optimization algorithm and compare it with typical improved algorithms to prove its effectiveness; finally, to solve the random initialization problem of IRELM, we use IPSO to optimize the initial weights and deviations of IRELM to improve the classification ability of IRELM. The experimental results show that IPSO-IRELM algorithm has better generalization ability, which not only improves the accuracy of intrusion detection, but also has certain recognition ability for minority class samples.
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