| Title | Machine Learning Based IoT Edge Node Security Attack and Countermeasures |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Laguduva, Vishalini, Islam, Sheikh Ariful, Aakur, Sathyanarayanan, Katkoori, Srinivas, Karam, Robert |
| Conference Name | 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) |
| Date Published | jul |
| Keywords | authentic PUFs, authentication, brute force attacks, cloned PUF devices, Cloning, cloning accuracy, cloud computing, cloud server, Computer architecture, computer network security, cryptography, highly connected ecosystem, human factors, Internet of Things, IoT, IoT devices, IoT edge node security attack, IoT networks, learning (artificial intelligence), machine learning, machine-learning based countermeasure, malicious attack, malicious PUF modeling, noninvasive architecture independent machine learning attack, physically unclonable functions, policy-based governance, Protocols, pubcrawl, PUF, PUF architecture, PUF designs, PUF structure, security, Servers, two-stage brute force attack model, ubiquitous computing devices |
| 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. |
| DOI | 10.1109/ISVLSI.2019.00124 |
| Citation Key | laguduva_machine_2019 |
Machine Learning Based IoT Edge Node Security Attack and Countermeasures