Automated Security Assessment for the Internet of Things

TL;DR

This paper introduces an automated framework combining machine learning, natural language processing, and graphical security models to efficiently assess IoT network security and identify critical attack paths.

Contribution

It presents a novel automated security assessment framework for IoT that predicts vulnerability metrics and models attack paths using a two-layered graphical approach.

Findings

Achieves over 90% accuracy in vulnerability metric prediction.

Effectively identifies the most vulnerable attack paths.

Demonstrates applicability on a real-world smart building IoT system.

Abstract

Internet of Things (IoT) based applications face an increasing number of potential security risks, which need to be systematically assessed and addressed. Expert-based manual assessment of IoT security is a predominant approach, which is usually inefficient. To address this problem, we propose an automated security assessment framework for IoT networks. Our framework first leverages machine learning and natural language processing to analyze vulnerability descriptions for predicting vulnerability metrics. The predicted metrics are then input into a two-layered graphical security model, which consists of an attack graph at the upper layer to present the network connectivity and an attack tree for each node in the network at the bottom layer to depict the vulnerability information. This security model automatically assesses the security of the IoT network by capturing potential attack paths. We evaluate the viability of our approach using a proof-of-concept smart building system model which contains a variety of real-world IoT devices and potential vulnerabilities. Our evaluation of the proposed framework demonstrates its effectiveness in terms of automatically predicting the vulnerability metrics of new vulnerabilities with more than 90% accuracy, on average, and identifying the most vulnerable attack paths within an IoT network. The produced assessment results can serve as a guideline for cybersecurity professionals to take further actions and mitigate risks in a timely manner.