Smart contracts hold digital coins worth billions of dollars, their security
issues have drawn extensive attention in the past years. Towards smart contract
vulnerability detection, conventional methods heavily rely on fixed expert
rules, leading to low accuracy and poor scalability. Recent deep learning
approaches alleviate this issue but fail to encode useful expert knowledge. In
this paper, we explore combining deep learning with expert patterns in an
explainable fashion. Specifically, we develop automatic tools to extract expert
patterns from the source code. We then cast the code into a semantic graph to
extract deep graph features. Thereafter, the global graph feature and local
expert patterns are fused to cooperate and approach the final prediction, while
yielding their interpretable weights. Experiments are conducted on all
available smart contracts with source code in two platforms, Ethereum and VNT
Chain. Empirically, our system significantly outperforms state-of-the-art
methods. Our code is released.

本文将深度学习和专家模式相结合，从源代码中提取专家模式，将代码转换成语义图，提取深度图特征，然后将全局图特征和本地专家模式融合以有效检测智能合约安全漏洞，实验证明该系统显著优于现有最先进的方法。

智能合约漏洞检测：从纯神经网络到可解释的图形特征和专家模式融合

Smart Contract Vulnerability Detection: From Pure Neural Network to  Interpretable Graph Feature and Expert Pattern Fusion

Static analysis tools are widely used for vulnerability detection as they
understand programs with complex behavior and millions of lines of code.
Despite their popularity, static analysis tools are known to generate an excess
of false positives. The recent ability of Machine Learning models to understand
programming languages opens new possibilities when applied to static analysis.
However, existing datasets to train models for vulnerability identification
suffer from multiple limitations such as limited bug context, limited size, and
synthetic and unrealistic source code. We propose D2A, a differential analysis
based approach to label issues reported by static analysis tools. The D2A
dataset is built by analyzing version pairs from multiple open source projects.
From each project, we select bug fixing commits and we run static analysis on
the versions before and after such commits. If some issues detected in a
before-commit version disappear in the corresponding after-commit version, they
are very likely to be real bugs that got fixed by the commit. We use D2A to
generate a large labeled dataset to train models for vulnerability
identification. We show that the dataset can be used to build a classifier to
identify possible false alarms among the issues reported by static analysis,
hence helping developers prioritize and investigate potential true positives
first.

通过差分分析，建立一个新的数据集 D2A，并利用机器学习对静态分析工具生成的漏洞报告进行分类，从而辅助开发人员优先调查和处理真实存在的问题。

D2A: 使用差分分析建立的供基于人工智能漏洞检测方法使用的数据集

D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using  Differential Analysis

This work explores the signal awareness of AI models for source code
understanding. Using a software vulnerability detection use case, we evaluate
the models' ability to capture the correct vulnerability signals to produce
their predictions. Our prediction-preserving input minimization (P2IM) approach
systematically reduces the original source code to a minimal snippet which a
model needs to maintain its prediction. The model's reliance on incorrect
signals is then uncovered when the vulnerability in the original code is
missing in the minimal snippet, both of which the model however predicts as
being vulnerable. We measure the signal awareness of models using a new metric
we propose- Signal-aware Recall (SAR). We apply P2IM on three different neural
network architectures across multiple datasets. The results show a sharp drop
in the model's Recall from the high 90s to sub-60s with the new metric,
highlighting that the models are presumably picking up a lot of noise or
dataset nuances while learning their vulnerability detection logic. Although
the drop in model performance may be perceived as an adversarial attack, but
this isn't P2IM's objective. The idea is rather to uncover the signal-awareness
of a black-box model in a data-driven manner via controlled queries. SAR's
purpose is to measure the impact of task-agnostic model training, and not to
suggest a shortcoming in the Recall metric. The expectation, in fact, is for
SAR to match Recall in the ideal scenario where the model truly captures
task-specific signals.

本研究探讨了 AI 模型对源代码理解的信号意识。使用软件漏洞检测用例，评估模型捕获正确漏洞信号以生成预测的能力，并通过预测保留输入的最小段落，揭示模型在学习漏洞检测逻辑时捕获的错误信号。通过提出的 “信号感知召回率”（SAR）进行模型信号感知度的测量，并在多个数据集上应用 P2IM 三种不同的神经网络架构。结果表明，模型的 SAR 从 90 多陡峭下降到低于 60，突显了模型在学习漏洞检测逻辑时可能会捕获很多噪声或数据集细微差异。