The rapid growth of Ethereum has spurred widespread adoption of smart contracts, enabling substantial financial transactions. Once deployed on the blockchain, smart contracts are immutable, rendering them unmodifiable even if vulnerabilities are present. In recent years, numerous attacks exploiting these vulnerabilities have caused significant financial losses. Although prior research has improved vulnerability detection in source code or bytecode before deployment, identifying attacks that exploit vulnerabilities during the execution phase after deployment remains a significant challenge. These challenges arise from the limited adaptability of predefined detection rules and an overreliance on opcode sequence names, which often neglects a comprehensive analysis of opcode sequence properties. In this study, we propose an advanced multidimensional feature fusion technique designed to detect attacks during the execution phase of smart contracts. By leveraging deep learning, our approach enhances detection accuracy through a comprehensive analysis of attack behaviors across four dimensions: operation objects, action behaviors, functional categories, and gas consumption. Extensive experiments demonstrate that our method achieves a detection accuracy of 97.21% and a weighted F1-score of 97.21%, confirming its effectiveness in identifying attacks.

Abstract With the rapid evolution of blockchain technologies, Ethereum has emerged as a central platform for advanced financial applications but has concurrently experienced a rise in security vulnerabilities, particularly from front-running attacks. These attacks exploit transaction sequencing for illegal gains. To combat this, we introduce FRACE (Front-Running Attack Classification using Ensemble Learning), a novel methodology that classifies front-running attacks into displacement, insertion, and suppression using an ensemble learning model. This precise classification facilitates tailored defensive strategies, enhancing the robustness and accuracy of attack detection. Our approach achieves an accuracy of 95.36% and an F1-score of 95.30%, significantly improving the security of decentralized applications. Extensive analysis and validation on Ethereum confirm these results. Future efforts will refine these models and extend their application to other blockchain platforms, striving for a universally secure, transparent, and reliable digital transaction ecosystem.

The exponential growth of Internet of Things (IoT) devices has led to the proposal of edge computing for data processing. The decentralized nature of edge computing servers and IoT devices makes blockchain ideal for connecting IoT users and servers. The consensus protocol, a core technology in blockchain, ensures node agreement and operational efficiency. However, as node numbers increase and spread geographically, traditional consensus protocols face deployment challenges, significantly reducing efficiency. To address this, we propose a hierarchical blockchain consensus protocol based on geographic location and a trust model called location and trust delegated-BFT (LT-DBFT). This protocol reduces global communication delays caused by wide geographic distribution by assigning consensus nodes to different clusters based on their locations. A trust model is also designed to elect active nodes for consensus voting, thereby reducing the consensus overhead within clusters. Subsequently, the primary nodes of each cluster form a global shared layer to achieve the final ordering and execution of transactions. Through theoretical and experimental analysis, our scheme demonstrates lower latency and higher throughput performance than traditional practical Byzantine fault tolerance and GeoBFT, making it more suitable for efficient deployment in large-scale and geographically widespread IoT environments.

Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.

Ethereum is a blockchain platform that allows developers to create smart contracts. Smart contracts are programs that can automatically execute and handle cryptocurrency funds. However, over a hundred thousand new smart contracts are deployed every day and inevitably contain vulnerabilities due to programming errors. Once deployed, smart contracts cannot be fixed or changed, leaving funds at risk. To mitigate it, we use deep learning to detect vulnerabilities in smart contracts. First, we create our own dataset of labeled smart contracts based on opcode sequences, since few smart contract codes and labeled datasets are publicly available. We collect opcode sequences by replaying real-world transactions from the Ethereum Mainnet in our fully synchronized node while we leverage a plugin called "SODA" to label opcode sequences with vulnerability classes. Second, after data collection, we preprocess the data by removing duplicate opcode sequences, normalizing the sequences to the same length, and converting them into vectors. Finally, to detect vulnerabilities in smart contracts, we train a deep classification model using LSTM neural networks. Our model achieved an average accuracy of 82.63% and an F1-score of 79.74% across seven types of vulnerabilities, which is important for securing funds and logic in smart contracts.

This article proposes a multilateral multienergy trading framework for synergetic hydrogen (H2) and electricity transactions among renewable-dominated hybrid charging stations (HCSs). In this framework, each autonomous HCS with various renewable energy resource (RES) endowment can harvest local renewables for internal green H2and electricity generation to simultaneously meet demands of electric vehicles (EVs) and hydrogen-powered vehicles (HVs) from the transportation network. The surplus electricity/H2production of the HCS is accommodated by external multilateral transactions to increase the additional profit. Besides, each HCS is modeled as a sustainable energy hub, and multiple hubs with multienergy transactions contribute toward a low-carbon energy-transport nexus. A partial differential equation model based on fluid dynamic theory is formed to capture the temporal and spatial dynamics of traffic flows for estimating the EV/HV loads at HCSs. Furthermore, a distributed multilateral pricing algorithm is developed to iteratively derive the optimal prices and quantities for transactive electricity and H2. Comparative studies corroborate the superiority of the proposed methodology on economic merits and RES accommodation.

Mobile ad-hoc networks (MANETs) usually contain sparse or even empty regions called holes. The local optimum problem will occur when routing packets meet holes in the network. In this paper, we propose a novel hole-reshaping routing protocol (HRR) in large-scale MANETs. It effectively solves the hole problem by regularizing a hole with an ellipse, and then locally broadcasting the hole information away from the hole. Simulation results show that the proposed protocol guarantees finding a short routing path with a small routing delay, which is a prerequisite to achieve scalability in large-scale networks.