The blockchain cross-chain is a significant technology for inter-chain interconnection and value transfer among different blockchain networks. Cross-chain overcomes the “information island” problem of the closed blockchain network and is increasingly applied to multiple critical areas such as finance and the internet of things (IoT). Blockchain can be divided into three main categories of blockchain networks: public blockchains, private blockchains, and consortium blockchains. However, there are differences in block structures, consensus mechanisms, and complex working mechanisms among heterogeneous blockchains. The fragility of the cross-chain system itself makes the cross-chain system face some potential security and privacy threats. This paper discusses security defects on the cross-chain implementation mechanism, and discusses the impact of the structural features of blockchain networks on cross-chain security. In terms of cross-chain intercommunication, a cross-chain attack can be divided into a multi-chain combination attack, native chain attack, and inter-chain attack diffusion. Then various security threats and attack paths faced by the cross-chain system are analyzed. At last, the corresponding security defense methods of cross-chain security threats and future research directions for cross-chain applications are put forward.

Semiconducting metal oxide-based nanowires (SMO-NWs) for gas sensors have been extensively studied for their extraordinary surface-to-volume ratio, high chemical and thermal stabilities, high sensitivity, and unique electronic, photonic and mechanical properties. In addition to improving the sensor response, vast developments have recently focused on the fundamental sensing mechanism, low power consumption, as well as novel applications. Herein, this review provides a state-of-art overview of electrically transduced gas sensors based on SMO-NWs. We first discuss the advanced synthesis and assembly techniques for high-quality SMO-NWs, the detailed sensor architectures, as well as the important gas-sensing performance. Relationships between the NWs structure and gas sensing performance are established by understanding general sensitization models related to size and shape, crystal defect, doped and loaded additive, and contact parameters. Moreover, major strategies for low-power gas sensors are proposed, including integrating NWs into microhotplates, self-heating operation, and designing room-temperature gas sensors. Emerging application areas of SMO-NWs-based gas sensors in disease diagnosis, environmental engineering, safety and security, flexible and wearable technology have also been studied. In the end, some insights into new challenges and future prospects for commercialization are highlighted.

With the rapid development of decentralized financial (DeFi), the total value locked (TVL) in DeFi continues to increase. A big number of adversaries exploit logic vulnerabilities to attack DeFi applications for profit, such as flash loan attacks and price manipulation attacks. However, the current vulnerability detection tools for smart contracts cannot be directly used to detect the logic vulnerabilities generated by the combination of different protocols. How to characterize and detect DeFi attacks that exploited logic vulnerabilities is a big challenge. In this work, we propose a deep-learning-based attack detection system on DeFi, called DeFiScanner, in which we design a novel neural network that includes a global model, a local model, and a fusion model to characterize DeFi attacks. First, the unstructured emitted events are automatically and efficiently normalized. Second, the transaction-related features of normalized emitted events are enriched with the global model and the semantic features of emitted events are extracted with the local model. Finally, the transaction-related features and the semantic features of emitted events are fused efficiently with the fusion model to detect DeFi attacks. We collect a dataset that consists of 50 910 real-world DeFi transactions on Ethereum (ETH). The extensive experimental results demonstrate the effectiveness of DeFiScanner. The true positive rate (TPR) and the area under the receiver operating characteristic (ROC) curve of the system reach 0.91 and 0.97, respectively.

Digital assets involved in smart contracts are on the rise. Security vulnerabilities in smart contracts have resulted in significant losses for the blockchain community. Existing smart contract vulnerability detection techniques have been typically single-purposed and focused only on the source code or opcode of contracts. This paper presents a new smart contract vulnerability detection method, which extracts features from different levels of smart contracts to train machine learning models for effective detection of vulnerabilities. Specifically, we propose to extract 2-gram features from the opcodes of smart contracts and token features from the source code using a pre-trained CodeBERT model, thereby capturing the semantic information of smart contracts at different levels. The 2-gram and token features are separately aggregated and then fused and input into machine-learning models to mine the vulnerability features of contracts. Over 10,266 smart contracts are used to verify the proposed method. Widespread reentrancy, timestamp dependence, and transaction-ordering dependence vulnerabilities are considered. Experiments show the fused features can help significantly improve smart contract vulnerability detection compared to the single-level features. The detection accuracy is as high as 98%, 98% and 94% for the three vulnerabilities, respectively. The average detection time is 0.99 second per contract, indicating the proposed method is suitable for automatic batch detection of vulnerabilities in smart contracts.

Blockchain technology has been widely used in digital currency, Internet of Things, and other important fields because of its decentralization, nontampering, and anonymity. The vigorous development of blockchain cannot be separated from the security guarantee. However, there are various security threats within the blockchain that have shown in the past to cause huge financial losses. This paper aims at studying the multi-level security threats existing in the Ethereum blockchain, and exploring the security protection schemes under multiple attack scenarios. There are ten attack scenarios studied in this paper, which are replay attack, short url attack, false top-up attack, transaction order dependence attack, integer overflow attack, re-entrancy attack, honeypot attack, airdrop hunting attack, writing of arbitrary storage address attack, and gas exhaustion denial of service attack. This paper also proposes protection schemes. Finally, these schemes are evaluated by experiments. Experimental results show that our approach is efficient and does not bring too much extra cost and that the time cost has doubled at most.

Delegated-Proof-of-Stake (DPoS) blockchains, such as EOSIO, Steem and TRON, are governed by a committee of block producers elected via a coin-based voting system. We recently witnessed the first de facto blockchain takeover that happened between Steem and TRON. Within one hour of this incident, TRON founder took over the entire Steem committee, forcing the original Steem community to leave the blockchain that they maintained for years. This is a historical event in the evolution of blockchains and Web 3.0. Despite its significant disruptive impact, little is known about how vulnerable DPoS blockchains are in general to takeovers and the ways in which we can improve their resistance to takeovers. In this paper, we demonstrate that the resistance of a DPoS blockchain to takeovers is governed by both the theoretical design and the actual use of its underlying coin-based voting governance system. When voters actively cooperate to resist potential takeovers, our theoretical analysis reveals that the current active resistance of DPoS blockchains is far below the theoretical upper bound. However in practice, voter preferences could be significantly different. This paper presents the first large-scale empirical study of the passive takeover resistance of EOSIO, Steem and TRON. Our study identifies the diversity in voter preferences and characterizes the impact of this diversity on takeover resistance. Through both theoretical and empirical analyses, our study provides novel insights into the security of coin-based voting governance and suggests potential ways to improve the takeover resistance of any blockchain that implements this governance model.