  The prosperity of Decentralized Finance (DeFi) unveils underlying risks, with
reported losses surpassing 3.2 billion USD between 2018 and 2022 due to
vulnerabilities in Decentralized Applications (DApps). One significant threat
is the Price Manipulation Attack (PMA) that alters asset prices during
transaction execution. As a result, PMA accounts for over 50 million USD in
losses. To address the urgent need for efficient PMA detection, this paper
introduces a novel detection service, DeFiGuard, using Graph Neural Networks
(GNNs). In this paper, we propose cash flow graphs with four distinct features,
which capture the trading behaviors from transactions. Moreover, DeFiGuard
integrates transaction parsing, graph construction, model training, and PMA
detection. Evaluations on a dataset of 208 PMA and 2,080 non-PMA transactions
show that DeFiGuard with GNN models outperforms the baseline in Accuracy, TPR,
FPR, and AUC-ROC. The results of ablation studies suggest that the combination
of the four proposed node features enhances DeFiGuard's efficacy. Moreover,
DeFiGuard classifies transactions within 0.892 to 5.317 seconds, which provides
sufficient time for the victims (DApps and users) to take action to rescue
their vulnerable funds. In conclusion, this research offers a significant step
towards safeguarding the DeFi landscape from PMAs using GNNs.
