Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Lightweight and Machine Learning Attack Resistant Physical Unclonable Functions
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

More and more embedded devices such as smart home appliances are being connected to the Internet. Implementing lightweight security at a low cost thus becomes increasingly relevant to prevent malicious network entries using less protected devices. Physical Unclonable Functions (PUFs), and more specifically Arbiter Physical Unclonable Functions (APUFs), are cryptographic primitives that have looked promising for achieving the mentioned requirements. Unfortunately, the APUF as well as many constructions based on it have either been shown weak to machine learning modeling attacks or are not sufficiently lightweight to fit on small embedded devices. Throughout the thesis, software called PyPuf has been used to simulate APUFs. By implementing file parsing in PyPuf it is now possible to generate a software model of an APUF realized in hardware. This thesis explores methods of protecting the APUF from machine learning modeling attacks. Together with a team of researchers at KTH, Royal Institute of Technology in Stockholm, I propose a lightweight PUF construction called the Cyclic Redundancy Check Physical Unclonable Function (CRC-PUF), in which inputs are obfuscated using a technique based on a Cyclic Redundancy Check (CRC). By changing the CRC generator polynomial between input evaluations, the probability of successfully recovering the obfuscated input is at most 2−86 for 128-bit inputs. The output protection technique of combining multiple APUF chains was also explored by comparing XOR with majority vote.

Abstract [sv]

Fler och fler inbyggda enheter så som smarta hushållsapparater ansluts till internet. Att implementera hårdvarueffektiv säkerhet till ett lågt pris blir därför mer och mer relevant för att förhindra illvilliga nätverksintrång av mindre skyddade enheter. Physical Unclonable Functions (PUFs), och mer specifikt Arbiter Physical Unclonable Functions (APUFs), är krypografiska primitiv som har sett lovande ut för att uppnå de nämnda kraven. Oturligt nog har APUF-konstruktionen, så väl som många andra konstruktioner som baseras på den antingen visats vara svaga mot modelleringsattacker baserade på maskininlärning, eller inte varit tillräckligt lättviktiga för att kunna användas på små inbyggda enheter. Under projektet har mjukvaran PyPuf använts för att simulera APUFs. Genom att implementera filparsning i PyPuf är det nu möjligt att generera en mjukvarumodel av en APUF realiserad i hårdvara. Denna avhandling undersöker metoder att försvara APUF-konstruktionen mot modelleringsattacker baserade på maskininlärning. Tillsammans med en grupp av forskare på KTH, Kungliga Tekniska Högskolan i Stockholm, föreslår jag en lättviktig PUF-konstruktion som kallas Cyclic Redundancy Check Physical Unclonable Function (CRCPUF), i vilken inmatningar döljs med hjälp av en teknik som är baserad på en Cyclic Redundancy Check (CRC). Genom att ändra generatorpolynomet hos CRC mellan inmatningsutvärderingar så minskar sannolikheten att framgångsrikt utvinna inmatningen till som mest 2−86 för 128bitarsinmatningar. Utmatningsskyddstekniken att kombinera flera APUF-kedjor var undersökt, genom att jämföra XOR med majoritetsomröstning.

Place, publisher, year, edition, pages
2019. , p. 40
Series
TRITA-EECS-EX ; 2019:653
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-264214OAI: oai:DiVA.org:kth-264214DiVA, id: diva2:1372527
Supervisors
Examiners
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-11-25Bibliographically approved

Open Access in DiVA

fulltext(1399 kB)6 downloads
File information
File name FULLTEXT01.pdfFile size 1399 kBChecksum SHA-512
cd8af039f6edf1ed6d4195ecd5147ef3b71e1b4488f14844771df899af40fc48c07c078916ae8662586fe40cd43f12c3caf702df7cce518fe9b7c709092ae6df
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering and Computer Science (EECS)
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 6 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 9 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf