Improved Seagull Optimization Algorithm to Optimize Neural Networks with Gated Recurrent Units for Network Intrusion Detection

In the field of network intrusion detection based on a neural network with Gated Recurrent Units (GRU), the accuracy of classification decreases due to the improper setting of the parameters. Therefore an Improved Seagull Optimization Algorithm (ISOA) to optimize GRU parameters has been proposed. The ISOA is proposed to address the shortcomings of the Seagull Optimization Algorithm (SOA) of low convergence accuracy and slow convergence speed in dealing with global optimization problems. The algorithm uses an opposition-based learning method instead of a random method to initialize the population so that the population has a better diversity. To balance the overall and local search capability of the algorithm, in this paper, we design a special factor update formula that converges nonlinearly with the number of iterations, allowing the algorithm to better jump out of locally optimal solutions and speed up convergence. The improved algorithm is used to obtain better parameters and uses them to set the parameters of the GRU to build a better-performing classification model. Multiple UCI datasets are used and compared with other methods and finally validated using the NSL-KDD datasets for network intrusion detection. The experimental results show that compared to the performance of long short-term memory(LSTM) units, multilayer perceptron(MLP), and the traditional neural network with GRU, the classification accuracy of the method with ISOA for optimizing GRU is improved on each dataset, which helps to improve the classification performance in network intrusion detection.