As the adoption of Internet of Things (IoT) devices continues to rise in enterprise environments, the need for effective and efficient security measures becomes increasingly critical. This paper presents a cost-efficient platform to facilitate the pre-deployment security checks of IoT devices by predicting potential weaknesses and associated attack patterns. The platform employs a Bidirectional Long Short-Term Memory (Bi-LSTM) network to analyse device-related textual data and predict weaknesses. At the same time, a Gradient Boosting Machine (GBM) model predicts likely attack patterns that could exploit these weaknesses. When evaluated on a dataset curated from the National Vulnerability Database (NVD) and publicly accessible IoT data sources, the system demonstrates high accuracy and reliability. The dataset created for this solution is publicly accessible.

翻译：随着物联网设备在企业环境中的持续普及，对高效安全措施的需求变得日益关键。本文提出了一种经济高效的平台，通过预测潜在弱点及相关攻击模式，以促进物联网设备的部署前安全检查。该平台采用双向长短期记忆网络分析设备相关的文本数据并预测弱点，同时利用梯度提升机模型预测可能利用这些弱点的攻击模式。在基于美国国家漏洞数据库及公开可访问的物联网数据源构建的数据集上进行评估时，该系统展现出较高的准确性与可靠性。为此解决方案创建的数据集已公开可访问。