Malicious software, called malware, can perform harmful actions on computer systems, which may cause economic damage and information leakage. Therefore, malware classification is meaningful and required to prevent malware attacks. Application programming interface (API) call sequences are easily observed and are good choices as features for malware classification. However, one of the main issues is how to generate a suitable feature for the algorithms of classification to achieve a high classification accuracy. Different malware sample brings API call sequence with different lengths, and these lengths may reach millions, which may cause computation cost and time complexities. Recurrent neural networks (RNNs) is one of the most versatile approaches to process time series data, which can be used to API call-based Malware calssification. In this paper, we propose a malware classification model with RNN, especially the long short-term memory (LSTM) and the gated recurrent unit (GRU), to classify variants of malware by using long-sequences of API calls. In numerical experiments, a benchmark dataset is used to illustrate the proposed approach and validate its accuracy. The numerical results show that the proposed RNN model works well on the malware classification.

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model’s interpretability and accuracy. This paper introduces an end-to-end deep learning model, named representation-enhanced knowledge graph convolutional networks (RKGCN), which dynamically analyses each user’s preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music.

The prediction of time series data, such as stock prices, is difficult since there exist many factors that affect the prediction model. Also, the influence of different factors on a stock price may be linear or nonlinear. The generation of good models for stock prices challenge the researchers in recent years. Long Short-Term Memory (LSTM) is a variation of Recurrent Neural Network (RNN), which can capture temporal sequence and have gained great success on time series prediction. Also, Convolutional Neural Network (CNN) is superior for extracting features from multi-dimensional sequences. In this paper, we propose a CNN-LSTM hybrid neural network with multiple factors to predict stock prices. Moreover, we add an attention mechanism to improve the scalability and the accuracy of the CNN-LSTM model. In the experiments, we compare our proposed model with different approaches in two real stock datasets. The results confirm the efficiency and scalability of our proposed method.

The de novo generation of hit-like molecules that show bioactivity and drug-likeness is an important task in computer-aided drug discovery. Although artificial intelligence can generate molecules with desired chemical properties, most previous studies have ignored the influence of disease-related cellular environments. This study proposes a novel deep generative model called GxVAEs to generate hit-like molecules from gene expression profiles by leveraging two joint variational autoencoders (VAEs). The first VAE, ProfileVAE, extracts latent features from gene expression profiles. The extracted features serve as the conditions that guide the second VAE, which is called MolVAE, in generating hit-like molecules. GxVAEs bridge the gap between molecular generation and the cellular environment in a biological system, and produce molecules that are biologically meaningful in the context of specific diseases. Experiments and case studies on the generation of therapeutic molecules show that GxVAEs outperforms current state-of-the-art baselines and yield hit-like molecules with potential bioactivity and drug-like properties. We were able to successfully generate the potential molecular structures with therapeutic effects for various diseases from patients’ disease profiles.