An optimization model of computer network security based on GABP neural network algorithm

Digital connectivity drives global innovation, ensuring computer network security is more critical than ever. With increasing interconnectivity, cyber threats are becoming more sophisticated, highlighting the urgent need for advanced defense mechanisms. This research proposed the investigation of the Genetic Algorithm with Back Propagation Neural Network (GA-BPNN) model for computer network safety. The investigation utilized the CIC-IDS-2017 dataset to evaluate the GA-BPNN model. Data preprocessing was performed using min–max normalization to standardize the dataset. Independent component analysis (ICA) was employed for feature extraction to improve the model’s efficiency. The GA-BPNN model was then implemented and analyzed for its performance in detecting network threats. The GA-BPNN approach demonstrated strong performance metrics for computer network security, achieving a recall (95%), F1 score (96.5%), precision (98.5%), and accuracy (98%). The GA-BPNN model enhances network security by improving threat detection and incident response. Its high accuracy and precision indicate its potential for strengthening cybersecurity defenses in interconnected systems. Implementing GA-BPNN in network security frameworks could lead to more effective protection against cyber threats. • Its analysis of the GA-BPNN model for computer network safety was suggested. The researchers collected data from the CIC-IDS-2017 dataset. • To safeguard data networks, the researchers preprocessed the data using min–max normalization and the GA-BPNN. • The GA-BPNN is the suggested method we utilize. Accuracy and precision are included in the GA-BPNN technique for computer network security, as well as recall rate and F1 score. • When applied to computer network security, the GA-BPNN technique may enhance threat identification, incident response, and cybersecurity.