In recent years, phishing scams have become the most serious type of crime
involved in Ethereum, the second-largest blockchain platform. The existing
phishing scams detection technology on Ethereum mostly uses traditional machine
learning or network representation learning to mine the key information from
the transaction network to identify phishing addresses. However, these methods
adopt the last transaction record or even completely ignore these records, and
only manual-designed features are taken for the node representation. In this
paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to
enhance phishing scams detection performance on Ethereum. Specifically, in the
temporal edges representation module, we model the temporal relationship of
historical transaction records between nodes to construct the edge
representation of the Ethereum transaction network. Moreover, the edge
representations around the node are aggregated to fuse topological interactive
relationships into its representation, also named as trading features, in the
edge2node module. We further combine trading features with common statistical
and structural features obtained by graph neural networks to identify phishing
addresses. Evaluated on real-world Ethereum phishing scams datasets, our TTAGN
(92.8% AUC, and 81.6% F1score) outperforms the state-of-the-art methods, and
the effectiveness of temporal edges representation and edge2node module is also
demonstrated.

本文提出了一种基于时间交易聚合图网络（TTAGN）的以太坊欺诈检测技术，通过模拟节点之间历史交易记录来构建以太坊交易网络的边表示，将边上的表示聚合到节点表示中，然后结合统计和结构特征进行识别，该方法在真实的以太坊欺诈数据集上优于同类方法。

TTAGN: 经时交易聚合图网络用于以太坊钓鱼诈骗检测

TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection

Recently, blockchain technology has become a topic in the spotlight but also
a hotbed of various cybercrimes. Among them, phishing scams on blockchain have
been found making a notable amount of money, thus emerging as a serious threat
to the trading security of the blockchain ecosystem. In order to create a
favorable environment for investment, an effective method for detecting
phishing scams is urgently needed in the blockchain ecosystem. To this end,
this paper proposes an approach to detect phishing scams on Ethereum by mining
its transaction records. Specifically, we first crawl the labeled phishing
addresses from two authorized websites and reconstruct the transaction network
according to the collected transaction records. Then, by taking the transaction
amount and timestamp into consideration, we propose a novel network embedding
algorithm called trans2vec to extract the features of the addresses for
subsequent phishing identification. Finally, we adopt the oneclass support
vector machine (SVM) to classify the nodes into normal and phishing ones.
Experimental results demonstrate that the phishing detection method works
effectively on Ethereum, and indicate the efficacy of trans2vec over existing
state-of-the-art algorithms on feature extraction for transaction networks.
This work is the first investigation on phishing detection on Ethereum via
network embedding and provides insights into how features of large-scale
transaction networks can be embedded.

通过交易记录的信息提取和网络嵌入技术，本文针对以太坊网络上的网络钓鱼现象提出了一种检测方法，并证明其在以太坊上的有效性和交易网络特征提取算法的优越性。