Use neural network algorithms to securely detect HTTP traffic

Abstract

HTTP traffic security detection is inseparable from the support of algorithms. However, in the traditional security detection methods, the classification ability of the algorithm is weak, resulting in detection results lower than expected. Therefore, based on neural network algorithm, a new HTTP traffic security detection method is studied. This research designs a collection module to collect HTTP traffic data. Structural features and statistical features are extracted to build a sensitive thesaurus. Based on neural network algorithm, HTTP traffic security is detected. The experimental results show that compared with the traditional detection methods, the detection method studied has better classification effect and the abnormal traffic data obtained is more accurate. It can be seen that under the application of neural network algorithm, the detection effect of the detection method has been further improved.