被动式设备指纹标记序列数据

通过被动式设备指纹技术，收集和分析用户设备的独特标记序列数据，主要用于与主动式设备指纹相结合，从而更加精确的识别用户设备与分析用户行为。在互联网安全行业中，有助于业内企业更准确地识别和理解用户行为，进行用户行为分析、安全监测等，从而提供定制化的服务和产品，增强行业竞争力。数据采集处理：从访问设备被动采集多维信息数据，如IP地址、操作系统版本、硬件架构、系统安装日期、设定语言、网络类型、TCP传输速率、屏幕分辨率等，数据清洗去重。
指纹计算：将收集到的各类数据信息转换为字节序列，将字节序列填充至448位模512的倍数，添加一个64位原始消息长度，确定特定常数量初始值A = 0x67452301，B = 0xefcdab89，C = 0x98badcfe，D = 0x10325476；将填充后数据分为512位消息块，每个消息块分为16个32位子块，对每个消息块执行64次迭代，每次迭代包括加法、位非、条件选择、位循环四个主要位操作，每次迭代使用不同的常数和位移量，并且根据迭代的序号选择不同的子块；同时在每次迭代中，根据位操作函数与逻辑运算更新ABCD四个常数量，完成迭代，将初始ABCD值与更新后的值进行加法运算，得到最终哈希值以32位的十六进制字符串形式输出，即设备指纹ID，该ID能够代表用户的设备而不会直接暴露用户个人信息。
数据应用：通过主动式设备指纹技术，收集和分析用户设备的独特标记序列数据，用于安全识别用户设备，有助于提供定制化服务和增强用户体验。

This dataset leverages passive device fingerprinting technology to collect and analyze unique marker sequence data of user devices, with the primary purpose of combining with active device fingerprinting to enable more precise user device identification and user behavior analysis. In the cybersecurity industry, this facilitates enterprises to more accurately identify and understand user behaviors, conduct user behavior analysis and security monitoring, thereby delivering customized services and products and enhancing industrial competitiveness.

Data Collection and Processing: Multi-dimensional information is passively collected from access devices, including but not limited to IP addresses, operating system versions, hardware architectures, system installation dates, configured system languages, network types, TCP transmission rates, screen resolutions, etc., followed by data cleaning and deduplication.

Fingerprint Calculation: Convert all collected data into byte sequences, pad the byte sequences to a length congruent to 448 bits modulo 512, and append a 64-bit value representing the original message length. Define the initial values of specific constants as A = 0x67452301, B = 0xefcdab89, C = 0x98badcfe, D = 0x10325476. Split the padded data into 512-bit message blocks, each of which is further divided into 16 32-bit sub-blocks. For each message block, 64 iterations are executed: each iteration encompasses four core bitwise operations, namely addition, bitwise NOT, conditional selection, and bitwise rotation, employs distinct constants and shift amounts, and selects different sub-blocks based on the iteration index. Meanwhile, during each iteration, the four constant values A, B, C, D are updated using bitwise operation functions and logical computations. Upon completion of all iterations for a given message block, the initial ABCD values of the block are added to the updated ABCD values to derive the final hash value, which is output as a 32-bit hexadecimal string referred to as the device fingerprint ID. This ID can uniquely represent the user's device without directly exposing any personal identifiable information of the user.

Data Application: Collect and analyze unique marker sequence data of user devices via active device fingerprinting technology, which is used for secure user device identification, helping to provide customized services and enhance user experience.

搜集汇总

数据集介绍

特点

该数据集包含6081条被动式设备指纹标记序列数据，每日更新，应用于互联网安全行业，主要用于用户行为分析和安全监测。数据通过特定算法生成设备指纹ID，有助于提供定制化服务和增强用户体验。