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].

IDC中国网络安全高级市场分析师王一汀表示，2024年，DDoS防护产品和服务市场增速有所放缓。与此同 时，T级DDoS攻击、智能化DDoS攻击、借助DDoS进行勒索等事件层出不穷，用户对于DDoS防护产品的能力 提出了更高的要求。未来，云原生化、智能化、平台化、一体化、服务化的DDoS产品服务将受到用户青睐。 智通财经APP获悉，近年来，分布式拒绝服务(DDoS)攻击的威胁形势日益严峻。IDC全球网络安全服务调研报 告显示，全球35%的CIO认为DDoS攻击缓解对于其组织网络安全防御具有重要意义，其中20%的CIO将持续增 加其在DDoS缓解上的投资。IDC数据显示：2024年，中国公有云抗DDoS市场规模约为23亿元人民币，规模同 比增长5.5%。除国内业务外，各技术服务提供商积极出海，在海外取得了较为喜人的市场结果。其中，中国电 信、阿里云、腾讯云、华为云、网宿安全在激烈的市场竞争中占据了主导地位。 IDC数据显示：2024年，中国抗DDoS硬件安全产品市场规模约为6.8亿元人民币，规模同比增长2%，受多种因 素影响，增速整体放缓。其中，华为、绿盟科技(300369.SZ)、迪普科技(300768.SZ ...