IoT-ID: A Novel Device-Specific Identifier Based on Unique Hardware Fingerprints

Vaidya, Girish; Nambi, Akshay; Prabhakar, T. V.; T, Vasanth Kumar; Sudhakara, Suhas

doi:10.1109/iotdi49375.2020.00026

Cited by 14 publications

(16 citation statements)

References 18 publications

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“…The devices falling within the same bin could be differentiated further by accumulating the clock counts and considering multiple clock sources, as discussed in Section 2.5. The Monte Carlo simulations of a ring oscillator with 0.18um devices from UMC report a similar frequency variation [47].…”

Section: The Theory Behind Acoustic Pufmentioning

confidence: 67%

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Sensor Identification via Acoustic Physically Unclonable Function

et al. 2022

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The supply chain traceability of components from a production facility to deployment and maintenance depends upon its irrefutable identity. There are two well-known methods for identification which includes an identity code stored in the memory and embedding a custom identification hardware. While storing the identity code is susceptible to malicious and unintentional attacks, the approach of embedding a custom identification hardware is infeasible for sensor nodes assembled with Commercially-Off-the-Shelf (COTS) devices. We propose a novel identifier - Acoustic PUF based on the innate properties of the sensor node. Acoustic PUF combines the uniqueness component and the position component of the sensor device signature. The uniqueness component is derived by exploiting the manufacturing tolerances, thus making the signature unclonable. The position component is derived through acoustic fingerprinting, thus giving a sticky identity to the sensor device. We evaluate Acoustic PUF for Uniqueness, Repeatability and Position identity with a deployment spanning several weeks. Through our experimental evaluation and further numerical analysis, we prove that Acoustic PUF can uniquely identify thousands of devices with 99% accuracy while simultaneously detecting the change in position.

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Section: The Theory Behind Acoustic Pufmentioning

confidence: 67%

“…When N number of accumulations are performed, while the mean scales linearly with N, the standard deviation scales √ 𝑁 . Thus, the coefficient of variation decreases to improve the repeatability [47]. In summary, improved separation could be achieved by increasing the timer frequency and accumulation count.…”

Section: Accumulation Countmentioning

confidence: 99%

Sensor Identification via Acoustic Physically Unclonable Function

et al. 2022

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“…Vaidya et. al [22] proposes, IoT-ID, a device-specific identifier, that cap-tures the device characteristics and can be used towards device identification. IoT-ID is based on physically unclonable functions (PUFs), that exploit variations in the manufacturing process to derive a unique fingerprint for integrated circuits.…”

Section: Related Workmentioning

confidence: 99%

BULWARK: A Framework to Store IoT Data in User Accounts

Reed

Tosun

2022

IEEE Access

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The explosive growth of the Internet of Things (IoT) devices raises serious concerns for a user's privacy and security because the existing software framework on these devices often support various default features and generate large data sets. Moreover, many IoT devices incorporate a manufacturer-owned cloud-based back-end support to process and store the generated data while simultaneously sharing with third parties. Clearly, in such an industry-driven environment with the desire to use the IoT data as a revenue stream, it is a challenge for users to control IoT data. Device manufacturers utilize an opaque software design where user data is generated and stored with little transparency. Manufacturers use EULAs as a legal construct to protect a manufacturer's legal standing and to explain a device's behavior, however this explanation is vague and lacks the necessary details for a user to determine a device's acceptable use and it has become increasingly difficult for users to secure and maintain their data. Fortunately, as the privacy minded user base of IoT devices grows, the manufacturers will be forced to implement a new framework that can enable users to have more control on the creation of their IoT data, and to store/disseminate such data in a secure and private manner. In this paper, we address this lack of transparency from manufacturers and address the issues of privacy and security by proposing a new framework called Bulwark, for manufacturer use on IoT devices and mobile applications. Proposed framework enables the user to generate and manage a set of data controlling rules, and store the result in their personal cloud account, while providing a dashboard data reporting tool enabling data transparency and supporting good user choices. The user's ability to access, disseminate and secure IoT generated data, is now available within our proposed framework. Using reverse engineering, simulation and implementation of open source solutions, we demonstrate support for a set of common devices. Each device executed the framework, while communicating with a mobile application and cloud services. Rules were generated for each message and telemetry was returned to the mobile application for dashboard rendering. We stored generated data in the cloud using our own account, while maintaining the free tier for each of the cloud services. Network usage increased between 4% and 9% while storage size grew between 0% and 2% larger, as compared to using the device without the framework. Our framework demonstrates support for a multitude of devices, by either open source or support for similar feature sets. This framework is easy to integrate and we anticipate wide spread adoption.

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“…The researchers, in [34] have presented a novel and robust device-specific identifier called IOT-ID, derived based on Physically Unclonable Functions (PUFs), which mainly gives each integrated circuit a unique "fingerprint". The (PUF) acts as the basic building block for IOT-ID that is constructed by combining features from a clock and ADC PUFs, viz, clock count , ADC signal , and ADC diff .…”

Section: Identification Using the Fingerprints Of Thingsmentioning

confidence: 99%

“…Moreover, they are implemented as a two stages edge machine learning (ML) model, which is first used to gather and store the features of IOT-ID for each instance, then IOT-ID had been trained for all devices to identify the device instance accurately. Furthermore, a new IOT-ID is then compared with the trained model to identify the device [34].…”

Section: Identification Using the Fingerprints Of Thingsmentioning

confidence: 99%

A Review of Identity Methods of Internet of Things (IOT)

Bkheet¹,

Agbinya²

2021

AIT

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The Internet of Things (IOT) is a recent technology originating from the field of sensor networks. It has received significant attention because it is involved in most aspects of our daily lives. The IOT vision makes objects of various kinds become part of the Internet by assigning each object a unique identifier, enabling objects to communicate with each other in the same or different environments. IOT can collect, process, and exchange data via a data communication network. There are many methods for identifying objects; some have existed since the beginning of IOT innovation, such as Radio Frequency Identification (RFID), Barcode/2D code, IP address, Electronic Product Codes (EPC), etc. Continuous development in IOT domain and the large number of objects connected to the Internet daily require an improved identification method to cope with the rapid development in this field. Many modern methods have been proposed recently, based on various technologies such as computer vision, fingerprinting, and machine learning. This paper introduces an overview of IOT and discusses its fundamental elements; it mainly focuses on identification of IOT which is considered the main part that the IOT systems rely on. The paper discusses the existing identification methods for IOT. Moreover, it provides a review of the modern identification methods proposed in recent literature.

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IoT-ID: A Novel Device-Specific Identifier Based on Unique Hardware Fingerprints

Cited by 14 publications

References 18 publications

Sensor Identification via Acoustic Physically Unclonable Function

Sensor Identification via Acoustic Physically Unclonable Function

BULWARK: A Framework to Store IoT Data in User Accounts

A Review of Identity Methods of Internet of Things (IOT)

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