Online and offline collaborative abnormal traffic intelligent detection system based on elastic lightweight width learning algorithm

As the boost of information technology, network security issues have been increasingly prominent. Therefore, it is crucial for maintaining network security to establish an efficient abnormal traffic detection system. The study first explained the width learning algorithm, which was used as the basic framework to introduce the elastic lightweight and gated neural networks for optimization. Finally, an online abnormal traffic detection model and an offline abnormal traffic detection model were proposed. The experimental results showed that the fastest iteration of the online detection model was 190, the prediction accuracy was 96 %, the prediction error floated only between −0.01 and 0.01, and the shortest computing time was 2.012 s. The minimum iteration for the offline detection model was 200, with the abnormal flow detection error of 0.11. The lowest average absolute percentage error was 0.141 and the normalized root mean square error was 0.207. The lowest root mean square error reached 0.175, and the highest R 2 error was 0.884. In summary, the two proposed models have achieved significant improvements in the accuracy and efficiency of abnormal traffic detection, providing a feasible solution for network security. • Introducing resilient lightweight and gated neural networks for efficient anomalous traffic detection. • Development of both online and offline abnormal traffic detection models. • Online detection model iteration time is 190 times faster, with a prediction accuracy of 96 % and minimal prediction error. • The offline detection model has a low error with an R-squared error as high as 0.884. • Significant improvements in accuracy and efficiency provide a viable solution for network security challenges.