In view of the problems of high data dimension, redundant features and low detection accuracy in current DDoS attack detection, This paper proposes a DDoS attack detection model CNN-Trans based on the combination of Convolutional Neural Network (CNN) and Transformer. The detection model firstly uses CNN integrated with CBAM attention for feature extraction. The model can better learn the depth characteristics of the input data, and then use Transformer to train the processed data to get classification results. Experimental results show that the accuracy and other aspects of the proposed model CNN-Trans have been improved
After data preprocessing, the feature dimension of NSL-KDD dataset increases from 42 dimensions to 122 dimensions. High dimensional data will make it more difficult for the model to learn the characteristics of the data, and there will be a lot of redundant data in the data set. Therefore, this paper uses the deep belief network to reduce the dimension of the characteristics of the intrusion detection data set after data preprocessing, and uses the stacking algorithm as the classifier to construct the intrusion detection model. Through comparative experiments, it is proved that the model has good performance in the four evaluation indexes of accuracy, precision, recall and F1 score, and effectively improves the performance of intrusion detection model.
KEYWORDS: Data modeling, Detection and tracking algorithms, Data conversion, Web 2.0 technologies, Neural networks, Machine learning, Algorithms, Video, Thermodynamics, Switches
In the increasingly complex network environment, various attacks emerge one after another. Being able to accurately detect elephant flow and mouse flow plays an extremely important role in defending against large-scale network attacks. Aiming at some shortcomings of current methods for detecting elephant flow and mouse flow, this paper proposes a detection method based on information entropy and improved random forest. After preprocessing the data, first calculate the data feature score with information entropy, screen out the truly valuable features according to the score, then put them into the improved random forest classifier, and finally get the detection results. In this paper, the grid search algorithm is used to optimize the tree and depth of random forest tree, so that the detection results can be obtained quickly and accurately. Experiments show that due to the significant difference between the data characteristics of elephant flow and mouse flow, this method can effectively identify elephant flow and mouse flow. The accuracy rate is 97.93%, the precision rate is 99.99%, the recall rate is 97.91%, and the F1-score is 98.94%, which is improved compared with other algorithms.
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