Titlebook: Machine Learning for Cyber Security; Third International Xiaofeng Chen,Hongyang Yan,Xiangliang Zhang Conference proceedings 2020 Springer

MAIM

Classification of Malware Variant Based on Ensemble Learning,learning model. First, malware binary data was transformed into a gray-scale image and the GIST texture feature of the image was extracted. Then, KNN (K Nearest Neighbor) and RF (random forest) method are used as base learners and a malware variant classification model was proposed based on the voti

游行

有危險(xiǎn)

Spatio-Temporal Graph Convolutional Networks for DDoS Attack Detecting,ernet. Many DDoS detection algorithms based on machine learning technology have emerged in recent years, for example, the LUCID algorithm, GNN model. But considering that DDoS attacks are based on both time and space, these algorithms only considered time and ignored space. Besides, by piece network

esoteric

Cerebral Microbleeds Detection Based on 3D Convolutional Neural Network,n of the location and amount of CMBs in brain tissue is crucial for the diagnosis, prevention and treatment of related diseases, where traditional Convolutional Neural Network (CNN) has been applied but may fail to achieve high enough detection accuracy. To alleviate this issue, we utilize 3D Fully

Obsessed

Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network,f popular method. However, the performance of the traditional convolutional network is limited by the network depth. To improve the accuracy of liver tumor segmentation, we propose a cascaded deep fully convolutional network (DFCN) which uses ResNet as the basis network followed by side output layer

昏暗

Colonoscopy

crumble

考博

Outwit