Biblio

In the era of big data and artificial intelligence, computer networks have become an important infrastructure, and the Internet has become ubiquitous. The most basic property of computer networks is reachability. The needs of the modern Internet mainly include cost, performance, reliability, and security. However, even for experienced network engineers, it is very difficult to manually conFigure the network to meet the needs of the modern large-scale Internet. The engineers often make mistakes, which can cause network paralysis, resulting in incalculable losses. Due to the development of automatic reasoning technology, automatic verification of network configuration is used to avoid mistakes. Network verification is at least an NP-C problem, so it is necessary to compress the network to reduce the network scale, thereby reducing the network verification time. This paper proposes a new model of network modeling, which is more suitable for the verification of network configuration on common protocols (such as RIP, BGP). On the basis of the existing compression method, two compression rules are added to compress the modeled network, reducing network verification time and conducting network reachability verification experiments on common networks. The experimental results are slightly better than the current compression methods.

Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges.