Towards Weaknesses and Attack Patterns Prediction for IoT Devices
<|reference_start|>Towards Weaknesses and Attack Patterns Prediction for IoT Devices: 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.<|reference_end|>
@article{a.2024towards,
    title={Towards Weaknesses and Attack Patterns Prediction for IoT Devices},
    author={Carlos A. Rivera A., Arash Shaghaghi, Gustavo Batista, Salil S.
  Kanhere},
    journal={arXiv preprint arXiv:2408.13172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13172},
    primaryClass={cs.CR}
}