Intrusion Detection System (IDS) can reduce the losses caused by intrusion behaviors and protect users’ information security. The effectiveness of IDS depends on the performance of the algorithm used in identifying intrusions. And traditional machine learning algorithms are limited to deal with the intrusion data with the characteristics of high-dimensionality, nonlinearity and imbalance. Therefore, this paper proposes an Intrusion Detection algorithm based on Image Enhanced Convolutional Neural Network (ID-IE-CNN). Firstly, based on the image processing technology of deep learning, oversampling method is used to increase the amount of original data to achieve data balance. Secondly, the one-dimensional data is converted into two-dimensional image data, the convolutional layer and the pooling layer are used to extract the main features of the image to reduce the data dimensionality. Third, the Tanh function is introduced as an activation function to fit nonlinear data, a fully connected layer is used to integrate local information, and the generalization ability of the prediction model is improved by the Dropout method. Finally, the Softmax classifier is used to predict the behavior of intrusion detection. This paper uses the KDDCup99 data set and compares with other competitive algorithms. Both in the performance of binary classification and multi-classification, ID-IE-CNN is better than the compared algorithms, which verifies its superiority.
Aiming at the limitations of existing algorithms of network intrusion detection in dealing with complex data of imbalance and high dimensionality, this paper proposes an intrusion detection algorithm based on convolutional neural network (CNN) and Light Gradient Boosting Machine (LightGBM). First, the data-type conversion, oversampling technology and image data conversion are included in the data preprocessing to make the data balanced and adapt to the input format. Then, by the convolutional layer, pooling layer and fully connected layer of the CNN model, the main features are abstracted from the converted image data. Finally, data of the main features is used for training and testing the LightGBM model, so as to get the final classification results. This paper uses KDDCUP99 dataset to carry out multi-classification experiments. By comparing the experiments before and after balancing the dataset, and comparing with similar algorithms, it verifies the superiority of the proposed algorithm in the classification performance of intrusion detection, especially for the minority attack classes.
Higher levels of PM2.5 concentration are becoming the leading cause of hazy days in China. However, studies have shown that the variations of PM2.5 involve complicated physical and chemical processes, which make their accurate predictions challenging. Meanwhile, the forecast results from numerical models frequently deviate from observation values. The deep learning method is a good substitute for the prediction of mass time series data in the field of meteorology. In the present study, a framework for PM2.5 concentration prediction is presented based on a three-dimensional convolutional neural network (3DCNN) and long short term memory neural network (LSTM). Using preprocessing, correlation analysis, feature extraction, and transformation, spatiotemporal sequence data was generated. In the spatiotemporal feature extraction phase, 3DCNN was used to extract high-level spatial features, and LSTM was used to extract temporal features. In the prediction phase, full connect (FC) was used to combine spatial and temporal features. To examine the efficacy of the proposed model, the PM2.5 concentration data, meteorological observation data, and grid dataset collected at ten observation stations in the Beijing Meteorological Bureau (BMB) were used. After the performance evaluation was compared with several methods including this proposed model, support vector machine (SVM), and the existing PM2.5 forecast system in BMB, root mean square errors (RMSE) and mean absolute errors (MAE) were chosen as evaluation indicators. The experimental results showed that the proposed model performed the best, the minimum MAE value was 3.24μg/m3, and the minimum RMSE value was 13.56μg/m3 over the ten stations. In addition, the proposed model overcame the underestimation produced by the existing PM2.5 forecast system in BMB and demonstrated superior performance for different time lengths over a 24-hour period. The results also confirmed the effectiveness of the deep learning method in the prediction of PM2.5 concentration.
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