Abnormal Network Traffic Detection Based on Improved LOF Algorithm
Aiming at the problems such as the accuracy and time complexity of existing abnormal network traffic detection methods, an abnormal network traffic detection method based on improved LOF algorithm is proposed in this paper. By analyzing the characteristics of the parameter in the traditional LOF algorithm, the model is combined with the DBSCAN algorithm to achieve the adaptive dynamic adjustment of the parameter to the changed data and optimized the accuracy for network traffic scenarios. In addition, algorithm optimization for large amounts of data reduces the time consumption of the model. The experimental results show that the method based on the improved LOF algorithm is of great value in practical application.
2018 10th International Conference on Intelligent Human Machine Systems and Cybernetics (IHMSC)