Ethereum phishing scams have proven to be highly profitable in recent years, and pose a serious risk to the security of the blockchain ecosystem. Existing techniques for detecting phishing scams mostly model the transaction network at a very coarse-grained level. These methods rarely take into account the heterogeneity of the network, and do not consider multiple transactions over time between pairs of accounts. To this end, we model the Ethereum transaction network as a heterogeneous muiltidigraph and propose a novel graph embedding technique. Specifically, we use a temporal-weighted biased walking method based on the Jump-Stay strategy, which not only captures the properties of the dynamic transaction network more comprehensively, but also elegantly balances the distribution of different types of nodes. The superior performance of our model is shown through classification experiments on a real-world dataset of Ethereum transactions.

Online social media has evolved into an essential collaboration system for information exchange. Clickbait refers to sentences containing tricks to attract users to click for commercial profit. In this work, we propose Part-of-speech Enhanced Prompt Learning (PEPL) method for clickbait detection. Our method introduce prompt learning into clickbait detection to stimulate the rich knowledge in pre-trained language models by adding task-specific guidance. Additionally, we design a part-of-speech enhanced prompt to realize grammatical features guided semantic understanding. We conduct extensive experiments to evaluate the performance. The results indicate that our method performs best in low-resource and full-scale scenarios and is still effective in extreme few-shot settings. We also perform ablation and generalization studies to demonstrate the necessity of the proposed components and that our method could be applied to enhance other pre-trained language models. Our method can be adopted in social media and assist downstream tasks to achieve better user-friendly collaboration systems.

Decentralized cryptocurrencies are influential smart contract applications in the blockchain, drawing interest from industry and academia. The capacity to govern and manage token behavior provided by the token smart contract adds to thriving decentralized applications. However, token smart contracts face security challenges in technology weakness and manipulation risks. In this work, we briefly describe the manipulation risk and propose TokenAuditor, a fuzzing framework detecting those risks in token smart contracts. TokenAuditor constructs basic blocks based on the contract bytecodes and adopts the rarity selection and mutation strategy to generate test cases. The main idea is to select the test cases that have hit rare basic blocks since the fuzzing started as candidates and perform mutation operations on them. In our evaluation, TokenAudiotr discovered 664 manipulation risks of four types in 4021 real-world token contracts.

Once deployed, a decentralized blockchain system ensures that it will operate faithfully so that no one can interfere with or manipulate its predefined regulations, such as block size and block creation interval investigated in this paper. However, fixed regulations prevent that system from adapting to the change of the environment, such as increasing the underlying network capacity, and result in sub-optimal performance. For example, Bitcoin remains at 7 TPS (transactions per second), even operating over the current Internet. In this paper, we propose a new paradigm for defining the behavior of a consensus system, named as Meta-Regulation, which allows autonomous evolution of the system behavior. A meta-regulation adjusts the actual behavior of a consensus system in response to the changing capacity of the underlying infrastructure and the community of participants. We demonstrate the effectiveness of the proposed meta-regulation by achieving significantly improved throughput and latency for Bitcoin, adapted to the current capacity of the Internet. Our experimental results show that Meta-Regulation can achieve at least$7\times $performance improvement over Bitcoin network deployed in 2009, resulting in 49.7 TPS or 68% reduction confirmation latency by fully utilizing the bandwidth and the computing power of average network nodes.