This paper proposes deep convolution neural network-based malware classification approach. The proposed work is a transfer learning approach, where we have developed multiple deep learning classification models. The classification models are built by adapting multiple pre-trained convolutional neural networks, namely; Xception, VGG19, InceptionResNetV2, MobileNet, InceptionV3, DenseNet, and ResNet50. In the current work, weights of pre-trained models are embellished by adding three fully connected (FC) layers. The proposed models have been evaluated on two different malware datasets, Microsoft and MalImg, consisting of malware images. The focus of this paper is to analyse the performance of fine-tuned CNN models for malware classification. The results of our experiments show that InceptionResNetV2 and Xception models have performed considerably well for the Microsoft dataset with accuracy equal to 96% and 95%, respectively. In the case of the MalImg dataset, InceptionResNetV2, InceptionV3, and Xception models have achieved excellent performance with an accuracy of up to 96%.

Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios. However, while designing these systems, machine learning approaches suffer from the problem of imbalanced data, i.e. imbalanced class distribution. Therefore, balancing the dataset becomes an imperative sub-task. Investigation of state-of-the-art approaches reveals that there is a need for a systematic study of class imbalance handling strategies to design an intelligent and capable system to detect the fraudulent transaction. This work aims to provide a comparative study of different class imbalance handling methods. To compare the effectiveness and efficiency of different class imbalance approaches in conjunction with state-of-the-art classification approaches, we have performed an extensive experimental study. We compared these methods on many performance indicators such as Precision, Recall, K-fold Cross-validation, AUC-ROC curve and execution time. In this study, we found that the Oversampling followed by Undersampling methods performs well for ensemble classification models such as AdaBoost, XGBoost and Random Forest.