  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.
