RP-NBSR: A Novel Network Attack Detection Model Based on Machine Learning

The rapid progress of the Internet has exposed networks to an increasednumber of threats. Intrusion detection technology can effectively protect networksecurity against malicious attacks. In this paper, we propose a ReliefF-P-NaiveBayes and softmax regression (RP-NBSR) model based on machine learningfor network attack detection to improve the false detection rate and F1 score ofunknown intrusion behavior. In the proposed model, the Pearson correlation coef-ficient is introduced to compensate for deficiencies in correlation analysis betweenfeatures by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm isused to preprocess the UNSW-NB15 dataset to remove irrelevant features andobtain a new feature subset. Finally, naïve Bayes and softmax regression (NBSR)classifier is constructed by cascading the naïve Bayes classifier and softmaxregression classifier, and an attack detection model based on RP-NBSR is established. The experimental results on the UNSW-NB15 dataset show that the attackdetection model based on RP-NBSR has a lower false detection rate and higherF1 score than other detection models.

Land Cover Classification of RADARSAT-2 SAR Data Using Convolutional Neural Network

In this paper,we propose a convolutional neural network（CNN）based on deep learning method for land cover classification of synthetic aperture radar（SAR）images.The proposed method consists of convolutional layers,pooling layers,a full connection layer and an output layer.The method acquires high-level abstractions for SAR data by using a hierarchical architecture composed of multiple non-linear transformations such as convolutions and poolings.The feature maps produced by convolutional layers are subsampled by pooling layers and then are converted into a feature vector by the full connection layer.The feature vector is then used by the output layer with softmax regression to perform land cover classification.The multi-layer method replaces hand-engineered features with backpropagation（BP）neural network algorithm for supervised feature learning,hierarchical feature extraction and land cover classification of SAR images.RADARSAT-2 ultra-fine beam high resolution HH-SAR images acquired in the rural urban fringe of the Greater Toronto Area（GTA）are selected for this study.The experiment results show that the accuracy of our classification method is about90%which is higher than that of nearest neighbor（NN）.