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Detection of Malicious PDF Files Using a Two-Stage Machine Learning Algorithm

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Chinese Journal of Electronics 2020年第6期29卷 1165-1177页

作者： HE Kang ZHU Yuefei HE Yubo LIU Long LU Bin LIN Wei State Key Laboratory of Mathematical Engineering and Advanced Computing

Portable document format(PDF) files are increasingly used to launch cyberattacks due to their popularity and increasing number of *** solutions have been developed to detect malicious files,but their accuracy decreases rapidly in face of new evasion *** explore how to improve the robustness of classifiers for detecting adversarial attacks in PDF *** replacement and the n-gram are implemented to extract robust features using proposed guiding *** the two-stage machine learning model,the objects are divided based on their types,and the anomaly detection model is first trained for each type *** former detection results are organized into tree-like information structure and treated as inputs to convolutional neural *** results show that the accuracy of our classifier is nearly 100% and the robustness against evasive samples is *** object features also enable the identification of different vulnerabilities exploited in malicious PDF files.

关键词： convolutional neural nets document handling feature extraction learning (artificial intelligence) pattern classification security of data trees (mathematics) robust feature extraction convolutional neural network tree-like information structure adversarial attack detection two-stage machine learning algorithm evasion techniques malicious files portable document format malicious PDF files classifier robustness anomaly detection model two-stage machine learning model Malicious PDF classification Robustness Guiding principles Two-stage classifier convolutional neural network

A Fast Evolutionary Learning to Optimize CNN

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Chinese Journal of Electronics 2020年第6期29卷 1061-1073页

作者： CHEN Jinyin LIN Xiang GAO Shangtengda XIONG Hui ZHANG Longyuan LIU Yi XUAN Qi College of Information Engineering Zhejiang University of Technology NESA Lab Zhejiang University

Deep neural networks(DNNs) show great performance in lots of *** neural network(CNN) is one of the classic DNNs,and various modified CNNs have been brought up,such as DenseNet,GoogleNet,ResNet,*** diverse tasks,a unique structure of CNN may show its ***,how to design an effective CNN model for a practical task is a *** this paper,we model the architecture optimization of CNN as an optimization problem and design a Genetic network programming based Fast evolutionary learning(GNP-FEL) to optimize ***-FEL contains three main ideas:First GNP is adopted to optimize CNN architecture and hyperparameters,which can build diverse network structures and make network parameters selfevolve;Second multi-objective optimization is designed by balancing both CNN model efficiency and structure compactness;Last a novel incremental training method is proposed to train offspring CNN models in GNP,which is capable of reducing time complexity *** have validated that GNP-FEL can quickly evolve a CNN classifier with a sufficiently compact *** the classifier has a comparable classification effect to state-ofthe-art CNN model.

关键词： convolutional neural nets genetic algorithms learning (artificial intelligence) neural net architecture optimize CNN deep neural networks convolutional neural network DNN diverse tasks CNN model practical task architecture optimization optimization problem genetic network programming GNP-FEL diverse network structures network parameters multiobjective optimization structure compactness offspring CNN models CNN classifier state-ofthe-art CNN model fast evolutionary learning Evolutionary learning Genetic network programming (GNP) CNN Multi-objective optimization Incremental training

Advancing Graph Convolution Network with Revised Laplacian Matrix

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Chinese Journal of Electronics 2020年第6期29卷 1134-1140页

作者： WANG Jiahui GUO Yi WANG Zhihong TANG Qifeng WEN Xinxiu East China University of Science and Technology National Engineering Laboratory for Big Data Distribution and Exchange Technologies

Graph convolution networks are extremely efficient on the graph-structure data,which both consider the graph and feature *** existing models mainly focus on redefining the complicated network structure,while ignoring the negative impact of lowquality input data during the aggregation *** paper utilizes the revised Laplacian matrix to improve the performance of the original model in the preprocessing *** comprehensive experimental results testify that our proposed model performs significantly better than other off-the-shelf models with a lower computational complexity,which gains relatively higher accuracy and stability.

关键词： computational complexity convolutional neural nets data structures graph theory matrix algebra graph-structure data feature information graph convolution network network structure computational complexity Laplacian matrix Graph convolution network Clustering Label propagation Laplacian matrix Graph structure Fraud detection

Deep Learning and Its Application in Diabetic Retinopathy Screening

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Chinese Journal of Electronics 2020年第6期29卷 992-1000页

作者： ZOU Beiji SHAN Xi ZHU Chengzhang DAI Yulan YUE Kejuan CHEN Yuanqiong XIAO Yalong HUANG Jiaer School of Computer Science and Engineering Central South University Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Central South University The College of Literature and Journalism Central South University

Deep learning(DL),especially convolutional neural networks(CNN),has gained wide popularity in various image processing *** the significant achievements obtained in DL,it has provided many successful solutions for real-world applications as well as in medical *** retinal images analysis has been widely applied to screening Diabetic retinopathy(DR),which can greatly help preventing the occurrence of complete blindness when used in the early *** this paper,we mainly focus on DL,and we will give an overview of the deep learning-based methods for DR ***,we will discuss the main issues encountered in the DR screening systems.

关键词： biomedical optical imaging convolutional neural nets diseases eye learning (artificial intelligence) medical image processing reviews complete blindness diabetic retinopathy screening systems deep learning automated retinal image analysis convolutional neural networks Deep learning convolutional neural networks Diabetic retinopathy screening Lesion detection