今天分享的是：慢雾科技SlowMist：2025年区块链加密资产追踪手册 报告共计：87页 《慢雾科技2025年区块链加密资产追踪手册》核心内容总结 近年来，加密行业链上犯罪频发，2024年至2025年上半年，区块链生态安全事件达531件，损失超43.86亿美元，Wallet Drainer钓 鱼攻击也造成约5.34亿美元损失。加密货币匿名性与区块链全球化属性，加大了跨境协查、司法互助和资产冻结难度，链上追踪 知识成为加密生态参与者的必修课，《区块链加密资产追踪手册》由此诞生。 手册先介绍链上追踪基础概念，涵盖主流公链与币种，如比特币基于UTXO模型、以太坊为智能合约平台且采用账户模型，还 有TRON、BNB Chain等，以及USDT、USDC等稳定币；阐述追踪核心概念，包括热钱包、冷钱包等不同类型钱包，充币地址、 合约地址等各类区块链地址，区块高度、交易哈希等交易结构元素，混币、兑换、跨链等操作，中心化平台、去中心化平台等 平台类型，还讲解了比特币的UTXO与找零机制。 接着介绍区块链浏览器，列举常用浏览器，以Etherscan为例说明其功能，可查询地址余额、代币持仓、交易详情等，还能分析 合约调用。专业 ...

Core Viewpoint - The article discusses the increasing security issues in blockchain technology, particularly focusing on DDoS attacks and how quantum convolutional neural networks (QCNN) developed by Micro Algorithm Technology (NASDAQ: MLGO) can enhance detection and response capabilities against these threats [1][7]. Group 1: Quantum Convolutional Neural Network (QCNN) Development - Micro Algorithm Technology has innovatively improved QCNN for detecting DDoS attacks in blockchain networks by optimizing quantum bit initialization and control methods, enhancing stability and reliability [1][7]. - The structure of QCNN has been adjusted to better handle blockchain transaction data and network status information, making it more suitable for the specific characteristics of blockchain data [1][7]. - Specialized quantum state reading and parsing technologies have been developed to accurately extract features related to DDoS attacks from quantum computation results [1][7]. Group 2: Data Collection and Preprocessing - Data collection involves gathering various types of data from the blockchain network, including transaction data, node status information, and network traffic data, using APIs and monitoring tools [3]. - Preprocessing of collected data is crucial for the effective operation of QCNN, involving data cleaning, noise reduction, and standardization to ensure data quality [3]. - Feature extraction is performed to identify characteristics related to DDoS attacks, such as transaction frequency and network traffic changes, which serve as inputs for the QCNN [3]. Group 3: Quantum Operations - Quantum bit initialization ensures that quantum bits are in a stable initial state, balancing the number of quantum bits with computational complexity [4]. - Quantum convolution operations utilize the properties of quantum bits to extract features and recognize patterns from input data through a series of quantum gate operations [4]. - Quantum pooling operations reduce data dimensions while retaining important features, employing a measurement-based pooling method to select the most probable quantum states [5]. Group 4: Classification and Output - After quantum convolution and pooling, a quantum fully connected layer processes the low-dimensional quantum state for DDoS attack classification and detection [6]. - The output from the quantum fully connected layer is a quantum state representing classification results, which is converted into a readable format using specialized quantum state reading techniques [6]. - If the probability distribution indicates a high likelihood of a DDoS attack, alerts are generated to notify network administrators for appropriate defensive actions [6]. Group 5: Applications and Future Prospects - The QCNN developed by Micro Algorithm Technology can monitor blockchain networks in real-time, promptly detecting signs of DDoS attacks and issuing alerts for immediate defensive measures [7]. - This technology can be integrated with other security measures, such as encryption and access control, to create a more secure blockchain environment [7]. - As quantum computing technology advances, the application prospects for QCNN in detecting DDoS attacks will expand, potentially enhancing computational power and accuracy [7].