In traffic accident, an accurate and timely severity prediction method is necessary for the successful deployment of an intelligent transportation system to provide corresponding levels of medical aid and transportation in a timely manner. Existing traffic accident’s severity prediction methods mainly use shallow severity prediction models and statistical models. To promote the prediction accuracy, a novel Traffic accident’s severity Prediction-Convolutional Neural Network (TASP-CNN) model for traffic accident’s severity prediction is proposed that considers combination relationships among traffic accident’s features. Based on the weights of traffic accident’s features, the Feature Matrix to Gray Image (FM2GI) algorithm is proposed to convert a single feature relationship of traffic accident’s data into gray images containing combination relationships in parallel as the input variables for the model. Moreover, experiments demonstrated that the proposed model for traffic accident’s severity prediction had a better performance.

Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine learning models. It systematically analyzes 1. the relationship between varying performance in machine learning models and imbalance rate (IR); 2. the performance stability of machine learning models on imbalanced binary data. In the proposed method, the imbalanced data augmentation algorithms are first designed to obtain the imbalanced dataset with gradually varying IR. Then, in order to obtain more objective classification results, the evaluation metric AFG, arithmetic mean of area under the receiver operating characteristic curve (AUC), F-measure and G-mean are used to evaluate the classification performance of machine learning models. Finally, based on AFG and coefficient of variation (CV), the performance stability evaluation method of machine learning models is proposed. Experiments of eight widely used machine learning models on 48 different imbalanced datasets demonstrate that the classification performance of machine learning models decreases with the increase of IR on the same imbalanced data. Meanwhile, the classification performances of LR, DT and SVC are unstable, while GNB, BNB, KNN, RF and GBDT are relatively stable and not susceptible to imbalanced data. In particular, the BNB has the most stable classification performance. The Friedman and Nemenyi post hoc statistical tests also confirmed this result. The SMOTE method is used in oversampling-based imbalanced data augmentation, and determining whether other oversampling methods can obtain consistent results needs further research. In the future, an imbalanced data augmentation algorithm based on undersampling and hybrid sampling should be used to analyze the performance impact of imbalanced binary data on machine learning models.

In the modern interconnected world, the popularization of networks and the rapid development of information technology led to the increasing security risks and threats in network systems. The existing intrusion detection system is constantly challenged by various malicious intrusion attacks. Machine learning algorithms have been widely used in intrusion detection. However, the model training requires the support of a sufficient high-quality samples, especially attack traffic data. Network intrusion detection datasets may not be shared between organizations due to data security and some privacy policy concerns. The federated learning framework is an optimal approach to address this issue, in which organizations collaborate to train a global model shared by multiple parties while keeping the data local to the client, guaranteeing the data privacy and security of all parties. However, there is a problem of class imbalance in the network traffic data owned by the organizations, which seriously affects the detection performance of the model and leads to a high consumption of model training time. Therefore, this study proposed a novel federated undersampling learning framework with Gini impurity, namely Fed-UGI. The framework is based on the hash-based block undersampling method to rebalance the client, which can solve the influence of imbalanced training data on the model detection performance and improve the model training efficiency. Moreover, the client weighted aggregation strategy based on Local Gini impurity can further optimize the effect of global model aggregation and reduce the impact of the dispersion degree and information difference in client data on model aggregation. In addition, extensive experiments on intrusion detection datasets show that compared to SOTA methods, the proposed Fed-UGI method has a good detection effect on the three metrics of F1-score, G-mean and AUC, the training time of the model is reduced by 51.76%-92.58%, especially in highly class imbalance situation.