Recognizing functions in binaries with neural networks

Binary analysis facilitates many important applications like malware detection and automatically fixing vulnerable software. In this paper, we propose to apply artificial neural networks to solve important yet difficult problems in binary analysis. Specifically, we tackle the problem of function identification, a crucial first step in many binary analysis techniques. Although neural networks have undergone a renaissance in the past few years, achieving breakthrough results in multiple application domains such as visual object recognition, language modeling, and speech recognition, no researchers have yet attempted to apply these techniques to problems in binary analysis. Using a dataset from prior work, we show that recurrent neural networks can identify functions in binaries with greater accuracy and efficiency than the state-of-the-art machine-learning-based method. We can train the model an order of magnitude faster and evaluate it on binaries hundreds of times faster. Furthermore, it halves the error rate on six out of eight benchmarks, and performs comparably on the remaining two.