Core Insights - The report by SlowMist highlights the increasing frequency of on-chain crimes in the cryptocurrency industry, with 531 security incidents reported from 2024 to the first half of 2025, resulting in losses exceeding $4.386 billion. Notably, Wallet Drainer phishing attacks alone caused approximately $534 million in losses [1][13]. Group 1: Introduction and Context - The rise in on-chain crimes includes various scams such as Ponzi schemes, phishing websites, and unauthorized access to exchanges, leading to significant financial losses [13]. - The anonymity of cryptocurrencies complicates the identification of malicious activities, making cross-border cooperation and asset freezing challenging [13][14]. - The report emphasizes the necessity for all participants in the crypto ecosystem to understand on-chain tracking knowledge as a fundamental skill [14][15]. Group 2: On-Chain Tracking Fundamentals - The handbook introduces basic concepts of on-chain tracking, covering major public chains and cryptocurrencies, including Bitcoin, Ethereum, TRON, and BNB Chain, as well as stablecoins like USDT and USDC [1][19]. - It explains core tracking concepts such as hot wallets, cold wallets, deposit addresses, contract addresses, transaction hashes, and various blockchain operations [1][25][26]. Group 3: Tools and Techniques for Tracking - The report discusses blockchain explorers, highlighting Etherscan's functionalities, which allow users to check address balances, token holdings, and transaction details [2]. - It introduces SlowMist's MistTrack tool, which features AML risk scoring and address tagging, supporting multi-chain tracking and cross-chain analysis [2][3]. - Common patterns of fund movement are analyzed, including peel chains, one-to-many distributions, and the use of mixers and cross-chain bridges [2][11]. Group 4: Response Strategies for Asset Theft - The handbook outlines measures to take in the event of asset theft, including loss prevention, preserving evidence, preliminary analysis, contacting professionals, and filing reports [2][11]. - It emphasizes the importance of multi-role collaboration in asset freezing and recovery efforts [2][11]. Group 5: Advanced Tracking Techniques - The report delves into tracking methods for cross-chain bridges, privacy tools, and NFTs, providing case studies for better understanding [2][11]. - It discusses address behavior analysis, including active behavior feature recognition and address clustering, and mentions the application of AI tools in on-chain analysis [2][11].

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].