标题： 使用生存和故障时间模型预测物联网设备漏洞修复时间 显示英文标题

标题： Predicting IoT Device Vulnerability Fix Times with Survival and Failure Time Models

摘要： 物联网（IoT）设备快速融入企业环境带来了显著的安全挑战。 许多物联网设备在上市时安全措施极少，通常每个设备平均包含25个漏洞。 为了增强网络安全措施并帮助系统管理员更有效地管理物联网补丁，我们提出了一种创新框架，用于预测有漏洞的物联网设备获得修复或补丁所需的时间。 我们开发了一个基于加速失效时间（AFT）方法的生存分析模型，使用XGBoost集成回归模型实现，以预测有漏洞的物联网设备何时会获得修复或补丁。 通过构建一个结合公共和私有来源的全面物联网漏洞数据库，我们提供了有关受影响设备、漏洞检测日期、已发布的CVE、补丁发布日期以及相关Twitter活动趋势的见解。 我们进行了详尽的实验，评估了不同特征组合的效果，包括基本的设备和漏洞数据、国家漏洞数据库（NVD）信息，如CVE、CWE和CVSS评分，将文本描述转换为句子向量，以及与CVE相关的Twitter趋势频率。 我们的实验表明，所提出的模型能够准确预测物联网漏洞的修复时间，其中来自VulDB和NVD的数据特别有效。 纳入Twitter趋势数据带来的额外好处有限。 该框架为组织提供了一个实用工具，用于预测漏洞解决时间，改进物联网补丁管理，并加强其应对潜在威胁的网络安全态势。 显示英文摘要

摘要： The rapid integration of Internet of Things (IoT) devices into enterprise environments presents significant security challenges. Many IoT devices are released to the market with minimal security measures, often harbouring an average of 25 vulnerabilities per device. To enhance cybersecurity measures and aid system administrators in managing IoT patches more effectively, we propose an innovative framework that predicts the time it will take for a vulnerable IoT device to receive a fix or patch. We developed a survival analysis model based on the Accelerated Failure Time (AFT) approach, implemented using the XGBoost ensemble regression model, to predict when vulnerable IoT devices will receive fixes or patches. By constructing a comprehensive IoT vulnerabilities database that combines public and private sources, we provide insights into affected devices, vulnerability detection dates, published CVEs, patch release dates, and associated Twitter activity trends. We conducted thorough experiments evaluating different combinations of features, including fundamental device and vulnerability data, National Vulnerability Database (NVD) information such as CVE, CWE, and CVSS scores, transformed textual descriptions into sentence vectors, and the frequency of Twitter trends related to CVEs. Our experiments demonstrate that the proposed model accurately predicts the time to fix for IoT vulnerabilities, with data from VulDB and NVD proving particularly effective. Incorporating Twitter trend data offered minimal additional benefit. This framework provides a practical tool for organisations to anticipate vulnerability resolutions, improve IoT patch management, and strengthen their cybersecurity posture against potential threats.