Towards Weaknesses and Attack Patterns Prediction for IoT Devices

• URL: http://arxiv.org/abs/2408.13172v1
• Date: Fri, 23 Aug 2024 15:43:51 GMT
• Title: Towards Weaknesses and Attack Patterns Prediction for IoT Devices
• Authors: Carlos A. Rivera A., Arash Shaghaghi, Gustavo Batista, Salil S. Kanhere,
• Abstract summary: This paper presents a cost-efficient platform to facilitate the pre-deployment security checks of IoT devices. 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.
• Score: 7.661561516558234
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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