ABSTRACT With the increasingly frequent attacks on software systems, software security is an issue that must be addressed. Within software security, automated detection of software vulnerabilities is an important subject. Most existing vulnerability detectors rely on the features of a single code type (e.g., source code or intermediate representation [IR]), which may lead to both the global features of the code slices and the memory operation information not being captured or considered. In particular, vulnerability detection based on source‐code features cannot usually include some macro or type definition content. In this paper, we propose a vulnerability‐detection method that combines the semantic features of source code and the low level virtual machine (LLVM) IR. Our proposed approach starts by slicing (C/C++) source files using improved slicing techniques to cover more comprehensive code information. It then extracts semantic information from the LLVM IR based on the executable source code. This can enrich the features fed to the artificial neural network (ANN) model for learning. We conducted an experimental evaluation using a publicly‐available dataset of 11,381 C/C++ programs. The experimental results show the vulnerability‐detection accuracy of our proposed method to reach over 96% for code slices generated according to four different slicing criteria. This outperforms most other compared detection methods.

With the rapid development of computer technology, the cross-subject network components (CSNC) is widely used in software. However, the existing of vulnerabilities in CSNC may seriously affect the security of software, which attracts the attention of software tester. This paper proposes a formal-based vulnerability detection method called FVDM for CSNC to detect the security vulnerabilities and defects in the logic of components. The proposed FVDM firstly selects the singleton as the medium of abstract computation as well as uses the formal description language to construct a vulnerability propagation model; And then, the FVDM classifies the vulnerabilities into explicit and implicit vulnerabilities through analyzing the types of vulnerabilities, thereby designing the vulnerability detection algorithm for explicit vulnerabilities and implicit vulnerabilities respectively. The experimental results on several COM (Component Object Model) components show that the proposed FVDM can detect the buffer overflow as well as illegal access vulnerabilities in the components.

Network intrusion detection plays an important role in the network security, but the increasingly complex network environment brings a serious challenge to intrusion detection. Although the existing efficient Convolutional Neural Network (CNN)-based network traffic intrusion detection models do not require manual design of the traffic features, but they do not make full use of the structured information of network traffic. In this paper, we propose a novel network intrusion detection model based on Temporal Convolutional Networks (TCN), it extracts the key features in the dataset through exploiting the characteristics of byte sequence in the network traffic packets. Compared with traditional recurrent neural networks (RNN), TCN shows a better performance in sequence modeling tasks and it can process the sequences in parallel for faster training. To solve the problem of poor detection accuracy caused by the “death” of some neurons on ReLU during the training stage, we use the ELU activation function in the TCN instead of ReLU. Finally, we compare our proposed TCN-based intrusion detection model with the state-of-the-art methods on the CTU public dataset, and the experimental results show that the use of TCN can obtain higher performance within less time consumption, in terms of higher average accuracy, higher average recall and higher average F1-measure.