Machine Learning based IoT Edge Node Security Attack and Countermeasures

TL;DR

This paper demonstrates a novel machine learning attack on PUF-based IoT security that can clone devices with high accuracy and proposes a discriminator countermeasure for remote authentication of IoT nodes.

Contribution

It introduces a non-invasive, architecture-independent ML attack on PUFs and a discriminator-based countermeasure for IoT device authentication.

Findings

Cloning accuracy of 93.5% for strong PUFs.

Discriminator achieves 96.01% accuracy in distinguishing cloned from authentic PUFs.

ML attack outperforms previous brute force methods by up to 48.31%.

Abstract

Advances in technology have enabled tremendous progress in the development of a highly connected ecosystem of ubiquitous computing devices collectively called the Internet of Things (IoT). Ensuring the security of IoT devices is a high priority due to the sensitive nature of the collected data. Physically Unclonable Functions (PUFs) have emerged as critical hardware primitive for ensuring the security of IoT nodes. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. Extant approaches to malicious PUF modeling assume that a priori knowledge and physical access to the PUF architecture is available for malicious attack on the IoT node. However, many IoT networks make the underlying assumption that the PUF architecture is sufficiently tamper-proof, both physically and mathematically. In this work, we show that knowledge of the underlying PUF structure is not necessary to clone a PUF. We present a novel non-invasive, architecture independent, machine learning attack for strong PUF designs with a cloning accuracy of 93.5% and improvements of up to 48.31% over an alternative, two-stage brute force attack model. We also propose a machine-learning based countermeasure, discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96.01%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.