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3.5 Hardware authentication and attack prevention

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Date: Tuesday 26 March 2019
Time: 14:30 - 16:00
Location / Room: Room 5

Chair:
Johanna Sepulveda, TU Munich, DE

Co-Chair:
Ilia Polian, University of Stuttgart, DE

Electronics industry involves considerable investment, which turns the protection of their Intellectual Property a main concern. The development of new technologies will depend on it. In this session, solutions based on obfuscated microfluidic biochips and PUF-like Quantum Dots (QD) devices are shown. Moreover, and attack that challenges PUF-based identifier techniques using machine learning is presented.

TimeLabelPresentation Title
Authors
14:303.5.1OPTICALLY INTERROGATED UNIQUE OBJECT WITH SIMULATION ATTACK PREVENTION
Speaker:
Povilas Marcinkevicius, Lancaster University, GB
Authors:
Povilas Marcinkevicius, Ibrahim Ethem Bagci, Nema M. Abdelazim, Christopher S. Woodhead, Robert J. Young and Utz Roedig, Lancaster University, GB
Abstract
A Unique Object (UNO) is a physical object with unique characteristics that can be measured externally. The usually analogue measurement can be converted into a digital representation - a fingerprint - which uniquely identifies the object. For practical applications it is necessary that measurements can be performed without the need of specialist equipment or complex measurement setup. Furthermore, a UNO should be able to defeat simulation attacks; an attacker may replace the UNO with a device or system that produces the expected measurement. Recently a novel type of UNOs based on Quantum Dots (QDs) and exhibiting unique photo-luminescence properties has been proposed. The uniqueness of these UNOs is based on quantum effects that can be interrogated using a light source and a camera. The so called Quantum Confinement UNO (QCUNO) responds uniquely to different light excitation levels which is exploited for simulation attack protection, as opposed to focusing on features too small to reproduce and therefore difficult to measure. In this paper we describe methods for extraction of fingerprints from the QCUNO. We evaluate our proposed methods using 46 UNOs in a controlled setup. Focus of the evaluation are entropy, error resilience and the ability to detect simulation attacks.
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15:003.5.2PUFS DEEP ATTACKS: ENHANCED MODELING ATTACKS USING DEEP LEARNING TECHNIQUES TO BREAK THE SECURITY OF DOUBLE ARBITER PUFS
Speaker:
Mahmoud Khalafalla, University Of Waterloo, CA
Authors:
Mahmoud Khalafalla and Catherine Gebotys, University of Waterloo, CA
Abstract
In the past decade and a half, physically unclonable functions (PUFs) have been introduced as a promising cryptographic primitive for hardware security applications. Since then, the race between proposing new complex PUF architectures and new attack schemes to break their security has been ongoing. Although modeling attacks using conventional machine learning techniques were successful against many PUFs, there are still some delay-based PUF architectures which remain unbroken against such attacks, such as the double arbiter PUFs. These stronger complex PUFs have the potential to be a promising candidate for key generation and authentication applications. This paper presents an in-depth analysis of modeling attacks using deep learning (DL) techniques against double arbiter PUFs (DAPUFs). Unlike more conventional machine learning techniques such as logistic regression and support vector machines, DL results show enhanced prediction accuracy of the attacked PUFs, thus pushing up the boundaries of modeling attacks to break more complex architectures. The attack on 3-1 DAPUFs has improved accuracy of over 86% (compared to previous research achieving a maximum of 76%) and the 4-1 DAPUFs accuracy ranges between 71%-81.5% (compared to previous research of maximum 63%). This research is crucial for analyzing the security of existing and future PUF architectures, confirming that as DL computations become more widely accessible, designers will need to hide the PUF's CRP relationship from attackers.
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15:303.5.3DESIEVE THE ATTACKER: THWARTING IP THEFT IN SIEVE-VALVE-BASED BIOCHIPS
Speaker:
shayan mohammed, new york university, US
Authors:
Mohammed Shayan1, Sukanta Bhattacharjee2, Yong Rafael Song2, Krishnendu Chakrabarty3 and Ramesh Karri4
1New York University, US; 2New York University Abu Dhabi, AE; 3Duke University, US; 4NYU, US
Abstract
Researchers develop bioassays following rigorous experimentation in the lab that involves considerable fiscal and highly-skilled-person-hour investment. Previous work shows that a bioassay implementation can be reverse engineered by using images or video and control signals of the biochip. Hence, techniques must be devised to protect the intellectual property (IP) rights of the bioassay developer. This study is the first step in this direction and it makes the following contributions: (1) it introduces a sieve-valve as the security primitive to obfuscate bioassay implementations; (2) it shows how sieve-valves can be used to obscure biochip building blocks such as multiplexers and mixers; (3) rules and metrics are presented for designing obfuscated biochips. We assess the cost-security trade-offs associated with this solution and demonstrate practical sieve-valve based obfuscation on real-life biochips.
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16:00End of session
Coffee Break in Exhibition Area



Coffee Breaks in the Exhibition Area

On all conference days (Tuesday to Thursday), coffee and tea will be served during the coffee breaks at the below-mentioned times in the exhibition area.

Lunch Breaks (Lunch Area)

On all conference days (Tuesday to Thursday), a seated lunch (lunch buffet) will be offered in the Lunch Area to fully registered conference delegates only. There will be badge control at the entrance to the lunch break area.

Tuesday, March 26, 2019

Wednesday, March 27, 2019

Thursday, March 28, 2019

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