Earticle

The Classifier Integration Model (CIM) with Convolutional Neural Networks (CNNs) as its local classifiers is applied to an object classification task for vehicle safety system in this paper. The Convolutional Neural Networks adopted in this paper has a very unique advantage when compared with conventional neural network models because CNNs do not require any feature extraction procedure prior to the classification process while other existing classification methods require rather very complex feature extraction process. Several CNN models are first designed as local classifiers and these models are then combined to make a decision in Classifier Integration Model for our classification task. Experiments on real data sets obtained for our experiments show that the CNN-based CIM scheme gives a promising performance in terms of training speed and classification accuracy.

With development of scale, diversity and complexity of network traffic, the drawbacks of traditional machine learning methods on traffic classification is gradually exposed, especially the false positive problem in large-scale real network traffic classification is particularly serious. In this paper, aiming at reducing the false positive rate of network traffic classification, an effective network traffic classification method --- CMM method. CMM method contains three steps, including dividing the training set into clusters, forming sub-classifiers, and classifier integration in accordance with the principle of minimization and maximization. In this paper, we firstly demonstrate the effectiveness of this method in reducing the false positive rate. Secondly, we conduct experiments in large-scale national backbone network, such as the SSL protocol classification and experimental results verify the effectiveness of this method in large-scale the actual network traffic classification.