Towards Weaknesses and Attack Patterns Prediction for IoT Devices

TL;DR

This paper introduces a cost-effective platform that uses machine learning models to predict potential security weaknesses and attack patterns in IoT devices before deployment, enhancing security measures.

Contribution

It presents a novel integrated approach combining Bi-LSTM and GBM models for pre-deployment security assessment of IoT devices, with publicly available datasets.

Findings

High accuracy in predicting device weaknesses

Effective attack pattern identification

Cost-efficient pre-deployment security checks

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.