Exploiting Breadth in Energy Datasets for Automated Device Identification

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

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Proof-of-PUF Enabled Blockchain: Concurrent Data and Device Security for Internet-of-Energy

Sensors ◽

10.3390/s21010028 ◽

2020 ◽

Vol 21 (1) ◽

pp. 28

Author(s):

Rameez Asif ◽

Kinan Ghanem ◽

James Irvine

Keyword(s):

Hybrid Approach ◽

Future Research ◽

Data Provenance ◽

Data Generation ◽

Cyber Attack ◽

Design Development ◽

Seamless Integration ◽

Device Identification ◽

Physical Unclonable Functions ◽

History Of

A detailed review on the technological aspects of Blockchain and Physical Unclonable Functions (PUFs) is presented in this article. It stipulates an emerging concept of Blockchain that integrates hardware security primitives via PUFs to solve bandwidth, integration, scalability, latency, and energy requirements for the Internet-of-Energy (IoE) systems. This hybrid approach, hereinafter termed as PUFChain, provides device and data provenance which records data origins, history of data generation and processing, and clone-proof device identification and authentication, thus possible to track the sources and reasons of any cyber attack. In addition to this, we review the key areas of design, development, and implementation, which will give us the insight on seamless integration with legacy IoE systems, reliability, cyber resilience, and future research challenges.

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Wireless device identification using oscillator control voltage as RF fingerprint

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) ◽

10.1109/ccece.2017.7946820 ◽

2017 ◽

Cited By ~ 3

Author(s):

Mahzad Azarmehr ◽

Ankit Mehta ◽

Rashid Rashidzadeh

Keyword(s):

Control Voltage ◽

Device Identification ◽

Wireless Device ◽

Rf Fingerprint

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Electromagnetic radiation-based IC device identification and verification using deep learning

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01808-z ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Hong-xin Zhang ◽

Jia Liu ◽

Jun Xu ◽

Fan Zhang ◽

Xiao-tong Cui ◽

...

Keyword(s):

Radio Frequency ◽

Electromagnetic Radiation ◽

Integrated Circuit ◽

Information Leakage ◽

Physical Layer ◽

Descent Method ◽

Gradient Descent Method ◽

Absolute Level ◽

Device Identification ◽

Multifactor Authentication

Abstract The electromagnetic radiation of electronic equipment carries information and can cause information leakage, which poses a serious threat to the security system; especially the information leakage caused by encryption or other important equipment will have more serious consequences. In the past decade or so, the attack technology and means for the physical layer have developed rapidly. And system designers have no effective method for this situation to eliminate or defend against threats with an absolute level of security. In recent years, device identification has been developed and improved as a physical-level technology to improve the security of integrated circuit (IC)-based multifactor authentication systems. Device identification tasks (including device identification and verification) are accomplished by monitoring and exploiting the characteristics of the IC’s unintentional electromagnetic radiation, without requiring any modification and process to hardware devices, thereby providing versatility and adapting existing hardware devices. Device identification based on deep residual networks and radio frequency is a technology applicable to the physical layer, which can improve the security of integrated circuit (IC)-based multifactor authentication systems. Device identification tasks (identification and verification) are accomplished by passively monitoring and utilizing the inherent properties of IC unintended RF transmissions without requiring any modifications to the analysis equipment. After the device performs a series of operations, the device is classified and identified using a deep residual neural network. The gradient descent method is used to adjust the network parameters, the batch training method is used to speed up the parameter tuning speed, the parameter regularization is used to improve the generalization, and finally, the Softmax classifier is used for classification. In the end, 28 chips of 4 models can be accurately identified into 4 categories, then the individual chips in each category can be identified, and finally 28 chips can be accurately identified, and the verification accuracy reached 100%. Therefore, the identification of radio frequency equipment based on deep residual network is very suitable as a countermeasure for implementing the device cloning technology and is expected to be related to various security issues.

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