Neural networks have been proved efficient in improving many machine learning tasks such
as convolutional neural networks and recurrent neural networks for computer vision and …

AI has provided us with the ability to automate tasks, extract information from vast amounts of
data, and synthesize media that is nearly indistinguishable from the real thing. However …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

Smart contract vulnerability detection draws extensive attention in recent years due to the
substantial losses caused by hacker attacks. Existing efforts for contract security analysis …

Vulnerability identification is crucial to protect the software systems from attacks for cyber
security. It is especially important to localize the vulnerable functions among the source code …

This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …

This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …

The ability to accurately compute the similarity between two pieces of binary code plays an
important role in a wide range of different problems. Several research communities such as …

Reverse engineering is a manually intensive but necessary technique for understanding the
inner workings of new malware, finding vulnerabilities in existing systems, and detecting …

The dramatic increase in the number of malware poses a serious challenge to the Android
platform and makes it difficult for malware analysis. In this paper, we propose a novel …