ICN intrusion detection method based on GA-CNN

The current industrial control system network is susceptible to data theft attacks such as SQL injection in practical applications, resulting in data loss or leakage of enterprise secrets. To solve the network intrusion problem faced by industrial control systems in the current global communication security environment, a network intrusion detection method based on genetic algorithm and improved convolutional neural network is proposed. Genetic algorithm is utilized to solve and optimize the data, one-dimensional multi-scale convolutional neural network is combined with gated recurrent unit to improve the network intrusion detection model, and finally the detection and defense of industrial control network intrusion is completed. GA is used to optimize the feature selection process to identify the key feature subsets that have the greatest impact on model performance. One-dimensional multi-scale convolutional neural network captures multi-scale features in network traffic data through multi-scale convolutional kernels, compensating for key features that traditional convolutional neural networks may overlook. The introduction of gated recurrent unit addresses the dependency of time series data and effectively solves the problem of gradient vanishing or exploding in traditional recurrent neural networks when processing long sequence data. The results showed that the proposed model only took about 8 seconds to complete training and testing, while all other models required about 10 seconds. The running time of the proposed method was less than that of other methods. In addition, the detection rate, packet loss rate, and false alarm rate of the proposed method for industrial control systems were 96.97%, 1.256%, and 0.0947% respectively, and the defense success rate of intrusion was higher than 90%. The results above show that the proposed method has very superior intrusion detection performance and good generalization ability and can meet the needs of industrial control systems for network intrusion detection.