Towards Weaknesses and Attack Patterns Prediction for IoT Devices
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929471583420416 |
|---|---|
| author | A., Carlos A. Rivera Shaghaghi, Arash Batista, Gustavo Kanhere, Salil S. |
| author_facet | A., Carlos A. Rivera Shaghaghi, Arash Batista, Gustavo Kanhere, Salil S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13172 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Weaknesses and Attack Patterns Prediction for IoT Devices A., Carlos A. Rivera Shaghaghi, Arash Batista, Gustavo Kanhere, Salil S. Cryptography and Security 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. |
| title | Towards Weaknesses and Attack Patterns Prediction for IoT Devices |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2408.13172 |