The phishing scams pose a serious threat to the ecosystem of Ethereum which is one of the largest blockchains in the world. Such a type of cyberattack recently has caused losses of millions of dollars. In this paper, we propose a Self-supervised IncrEmental deep Graph lEarning (SIEGE) model, for the phishing scam detection problem on Ethereum. To overcome the data scalability challenge, we propose splitting the original Ethereum transaction data and constructing transaction graphs for each split. Confronted with the minimal labeled data available, we resort to graph-based self-supervised learning. We design a spatial pretext task to learn high-quality node embeddings inside a single graph split, as well as an incremental learning paradigm and a temporal pretext task to facilitate information flow between different graph splits. To evaluate the effectiveness of SIEGE, we gather a real-world dataset consisting of six-month Ethereum transaction records. The results demonstrate that our model consistently outperforms baseline approaches in both transductive and inductive settings.

Transformer-based language models (e.g. BERT, RoBERT, GPT, etc) have shown remarkable performance in many natural language processing tasks and their multilingual variants make it easier to handle cross-lingual tasks without using machine translation system. In this paper, we apply multilingual BERT in cross-lingual information retrieval (CLIR) task with triplet loss to learn the relevance between queries and documents written in different languages. Moreover, we align the token embeddings from different languages via adversarial networks to help the language model to learn cross-lingual sentence representation. We achieve the state-of-the-art result on the newly published CLIR dataset: CLIRMatrix. Furthermore, we show that the adversarial multilingual BERT can also get the competitive result in the zero-shot setting in some specific languages when we are lack of CLIR training data in a specific language.