Biblio

In the computer field, cybersecurity has always been the focus of attention. How to detect malware is one of the focuses and difficulties in network security research effectively. Traditional existing malware detection schemes can be mainly divided into two methods categories: database matching and the machine learning method. With the rise of deep learning, more and more deep learning methods are applied in the field of malware detection. Deeper semantic features can be extracted via deep neural network. The main tasks of this paper are as follows: (1) Using machine learning methods and one-dimensional convolutional neural networks to detect malware (2) Propose a machine The method of combining learning and deep learning is used for detection. Machine learning uses LGBM to obtain an accuracy rate of 67.16%, and one-dimensional CNN obtains an accuracy rate of 72.47%. In (2), LGBM is used to screen the importance of features and then use a one-dimensional convolutional neural network, which helps to further improve the detection result has an accuracy rate of 78.64%.

With the rapid development of e-commerce and the increasing popularity of credit cards, online transactions have become increasingly smooth and convenient. However, many online transactions suffer from credit card fraud, resulting in huge losses every year. Many financial organizations and e-commerce companies are devoted to developing advanced fraud detection algorithms. This paper presents an approach to detect fraud transactions using the IEEE-CIS Fraud Detection dataset provided by Kaggle. Our stacked model is based on Gradient Boosting, LightGBM, CatBoost, and Random Forest. Besides, implementing StackNet improves the classification accuracy significantly and provides expandability to the network architecture. Our final model achieved an AUC of 0.9578 for the training set and 0.9325 for the validation set, demonstrating excellent performance in classifying different transaction types.