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.

Software rejuvenation is one of the proactive fault management techniques to prevent system performance degradation, which may lead to the system failure caused by software aging. In the design of software rejuvenation, it is important to determine the optimal timing of triggering the rejuvenation in terms of the system overhead. In this paper, we consider six software rejuvenation policies, which are categorized into time-based and workload-based policies, under the environment where the arrival stream of system follows a Markovian arrival process (MAP). After building the stochastic models with respective rejuvenation policies, we formulate the loss probability of transaction and the upper bound of mean response time as the system performance indices. In the numerical illustrations, we exhibit a comprehensive study to compare six software rejuvenation policies numerically and show that the proposed rejuvenation policies called wait-time policies are superior to the others under the MAP arrival stream.

Deep learning, especially graph neural networks (GNNs), provides efficient, fast, and automated methods to detect vulnerable code. However, the accuracy could be improved as previous studies were limited by existing code representations. Additionally, the diversity of embedding techniques and GNN models can make selecting the appropriate method challenging. Herein we propose Code Aggregate Graph (CAG) to improve vulnerability detection efficiency. CAG combines the principles of different code analyses such as abstract syntax tree, control flow graph, and program dependence graph with dominator and post-dominator trees. This extensive representation empowers deep graph networks for enhanced classification. We also implement different data encoding methods and neural networks to provide a multidimensional view of the system performance. Specifically, three word embedding approaches and three deep GNNs are utilized to build classifiers. Then CAG is evaluated using two datasets: a real-world open-source dataset and the software assurance reference dataset. CAG is also compared with seven state-of-the-art methods and six classic representations. CAG shows the best performance. Compared to previous studies, CAG has an increased accuracy (5.4%) and F1-score (5.1%). Additionally, experiments confirm that encoding has a positive impact on accuracy (4–6%) but the network type does not. The study should contribute to a meaningful benchmark for future research on code representations, data encoding, and GNNs.