| Popis: |
Intrusion detection systems (IDS) identify network intrusions by detecting abnormal traffic data, thereby ensuring network security. However, intrusion detection data can vary with changes in the network and attack environment, resulting in poor performance and portability of intrusion detection algorithms. Therefore, an intrusion detection method based on PSO-GA hyperparameter optimized ResNet-BiGRU is proposed. The two-layer bidirectional gated recurrent unit (BiGRU) is connected to the fully connected layer of the residual neural network (ResNet). Firstly, ResNet is used to extract parallel local features, and BiGRU is used to extract long-distance-dependent features from the parallel local features, and the attention mechanism is added after the BIGRU to utilize correlation between the features to assign weights to the extracted features, so as to more comprehensively capture the important features of network intrusion and improve the detection performance. At the same time, the parameters of the basic particle swarm optimization (PSO) are dynamically optimized and combined with the genetic algorithm (GA) to perform a mutation operation when the iterative process falls into a local optimal solution, adding a random perturbation to the current velocity and position of the particles, so that the particles are able to explore new regions in the space in order to jump out of the local optimal solution, and ultimately achieve automatic optimization of the hyperparameters of the ResNet-BiGRU model to achieve a model with better generalization performance. Finally, the proposed method is validated by using the variant NSL-KDD dataset, which achieves an accuracy of 98.46% and average precision, average recall and average False Alarm Rate(FAR) of 91.84%, 95.99% and 0.31%, and achieved high accuracy on three datasets KDD99, UNSW-NB15, and CIC-IDS 2017. The method is proved to have a strong intrusion detection capability by comparison experiments with other algorithms. |
