Binary Vulnerability Mining Based on Long Short-Term Memory Network

With the development of human society, the number of software and other computer products began to increase sharply, but the increase in the number of software also brought the increase of the application vulnerabilities behind. In recent years, due to the development of cloud computing and the improvement of computing power, deep learning began to be applied in the field of security, showing new competitiveness in binary program vulnerability detection. In this paper, the idea of natural language processing (NLP) is integrated into vulnerability detection. By extracting function blocks, word2vec is used to get the corresponding feature vector, and LSTM is used to train the obtained feature vector. Through experiments, we verified the potential of vulnerability detection based on deep learning in the future.