Towards Weaknesses and Attack Patterns Prediction for IoT Devices

2024/08/23 by Carlos A. Rivera A., Arash Shaghaghi, A., Carlos A. Rivera +5
Computer Science · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.2408.13172

openalex publication_date 2024/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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