Vulnerability detection with fine-grained interpretations 论文

摘要

Despite the successes of machine learning (ML) and deep learning (DL)-based vulnerability detectors (VD), they are limited to providing only the decision on whether a given code is vulnerable or not, without details on what part of the code is relevant to the detected vulnerability. We present IVDetect, an interpretable vulnerability detector with the philosophy of using Artificial Intelligence (AI) to detect vulnerabilities, while using Intelligence Assistant (IA) to provide VD interpretations in terms of vulnerable statements.