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.
@article{arxiv.2408.13172,
title = {Towards Weaknesses and Attack Patterns Prediction for IoT Devices},
author = {Carlos A. Rivera A. and Arash Shaghaghi and Gustavo Batista and Salil S. Kanhere},
journal= {arXiv preprint arXiv:2408.13172},
year = {2024}
}