Tamper Detection for Ubiquitous RFIDenabled Supply Chain

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Tamper Detection for Ubiquitous RFID-enabled
Supply Chain
2005 International Conference on Computational
Intelligence and Security, Xian, China December
15-19, 2005
Vidyasagar Potdar, Chen Wu, Elizabeth
Chang School of Information Systems Curtin
University of Technology Perth,
AUSTRALIA http//www.ceebi.curtin.edu.au/potdarv

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OUTLINE

• Introduction
• Issues
• Existing Solutions
• Proposed Solution
• Summary

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1. INTRODUCTION

• RFID Radio Frequency Identification
• Technology for automated data capture
• Composed of four major components
• Tag that contains the identification number
• Reader that activates the tag to broadcast its
  identification number
• Middleware that interacts with the reader and the
  backend database Molnar Wagner 2004, Hennig et
  al. 2005
• Backend Database

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1.1 RFID INFRASTUCTURE
RFID INFRASTRUCTURE
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1.2 APPLICATION DOMAINS

• Supply Chain Automation
• Asset Tracking
• Medical Applications
• People Tracking
• Manufacturing, Retail and Warehouse Tracking
• Livestock Tracking
• Tracking exact timing in sports events

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1.3 RFID INFRASTUCTURE
www.datasoft.se
www.eff.org
www.barcoding.com
www.kennedygrp.com
www.forrester.com
TAGS
READER
MIDDLEWARE
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1.4 TYPES OF RFID TAGS

• Types of RFID Tags
• Passive Tags
• Activated in presence of the readers radio
  waves.
• Active tags
• Self-activated using battery.

www.micromata.de
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1.5 EPC DATA STRUCTURE

• Electronic Product Code (EPC) was first developed
  by Auto-ID Center in MIT in 1999.
• This centre developed the initial RFID standard
  and later transferred to EPCGlobal for
  commercialization in late 2003.
• Two data structures were designed by EPC,
• 64 bit EPC was designed primarily for testing and
  
• 96 bit EPC were designed for commercialization.

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http//www.rsasecurity.com/rsalabs/rfid/images/cli
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1.6 EPC DATA STRUCTURE
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1.7 RFID Data Structure

• Header which determines which EAN.UCC key is used
  and how many bits are allocated to the remaining
  sections.
• EPC Manager which identifies the product
  manufacturer
• e.g. Toyota.
• Object Class which is a unique identifier for the
  product manufactured by the manufacturer
• e.g. Camry. This value is defined by the product
  manufacturer.
• Serial Number which is assigned to each item
  belonging to a class of product
• e.g. registration number 1AUT315. All the values
  assigned in these partitions are in binary format
  only.

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1.8 RFID MIDDLEWARE

• RFID Middleware locates itself between the RFID
  reader and the backend legacy systems.
• It manages the readers and extracts EPC data from
  the readers and sends the data to the enterprise
  WMS and the backend database.
• From the architectural perspective, RFID
  middleware has three layers of functionality
• Reader API
• Data Management, and
• Integration Management

www.irt.de
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1.9 RFID MIDDLEWARE
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2. ISSUES

• Security
• Cost
• Privacy
• Deployment
• Scalability
• Resilience
• Multi-modal Sensing

http//www.rochester.edu/in_visible_culture/issues
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2.1 ISSUES - SECURITY

• Security
• Data Tampering
• Insecure Wireless Communication
• No security mechanisms available on low end RFIDs

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2.2 ISSUES - COST

• Cost
• Low end RFIDs Affordable
• No Security Protocols
• High end RFIDs Very Expensive
• Comprehensive Security Mechanisms

www.piperreport.com/
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2.3 PROBLEM STATEMENT

• Data on the RFID is very significant and if this
  data is tampered it can have severe consequences.
  
• Data tampering of this nature needs to be
  detected as it can be a threat to national
  security.
• Data tampering can raise issues in collaborative
  environments where this data mismatch can result
  in repudiation issues.
• The main issue that this research tackles is to
  ascertain that data tampering has happened and to
  identify what data on the RFID is tampered.

www.cpapforseniors.com
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3. EXISTING SOLUTIONS

• Weis et al. (2004)
• Cryptographic properties in tags prevent
  unauthorized readers to read the RFID data.
• K. Chung (2004)
• Relational Check Code
• Henrici Müller (2004)
• Offers location privacy but it is not scalable
  because it requires a lot of cryptographic
  calculations

www.istockphoto.com/ imageindex/319/7/319788/
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3.1 ISSUES

• Most of the available literature focused on
  solutions based on next generation RFIDs.
• Such solutions assume cryptographic capabilities
  on the RFID tags which are currently very
  expensive.
• From a deployment perspective using next
  generation RFID tags is an expensive bet and
  assuring security on current generation RFID tags
  is still a major issue.
• Security issues in general and data tampering in
  particular has not been completely addressed in
  literature which gives us motivation to present
  our work.

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4. PROPOSED SOLUTION

• Proposed RFID Middleware Architecture
• Algorithm for Tamper Detection
• Embedding Algorithm
• Extraction and Detection Algorithm
• Discussion

www.adeasolutions.com
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4.1 Proposed RFID Middleware Architecture

• RFID Middleware with tamper detection component.
• Tamper detection layer is specially introduced to
  ascertain no data tampering has happened on the
  RFID tag.
• This is done to ascertain that whatever data is
  being propagated to the higher levels in the RFID
  middleware is tamper proof.
• It acts a measure of security and trust, which
  means if the data crosses the tamper detection
  component it is assured that it is not tampered
  and it can be reliably used for any further
  processing.

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4.2 Proposed RFID Middleware Architecture
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4.3 TAMPER DETECTION

• The proposed algorithms to detect tamper
  detection works by embedding secret information
  with in the RFID tags.
• In order to embed secret information we have to
  identify some space within the data which can be
  modified to represent secret information.
• In order to identify this space we investigated
  the RFID data structure.

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4.4 TAMPER DETECTION

• Header is used for identifying the EAN.UCC key
  and the partitioning scheme. Hence there is no
  redundant space.
• EPC Manager is used to identify the manufacturer
  uniquely. Hence this partition also doesnt offer
  any redundant space for embedding.
• Object Class is used to identify the product
  manufactured by the manufacturer. It may follow
  some product convention taxonomy where the first
  two digits might represent the classification of
  that product and so on. Hence there is no free
  space
• Serial Number is used to uniquely identify an
  item which belongs to a particular Object Class.
• It is orthogonal to first three partitions and
  can be decided by the manufacturer at will
  without violating any existing industry
  standards.
• Consequently it offers enough space to embed
  sufficient amount of data.
• The length of this partition is 38 bits (in
  EPC96) which offers enough room to accommodate
  the required amount of secret data.
• Thus this becomes most appropriate candidate for
  embedding the secret.

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4.5 EMBEDDING ALGORITHM

• The embedding algorithm begins by selecting a set
  of one way functions F f1, f2, f3.
• Each one way function is applied to the values
  within the RFID tags partition to generate a
  secret value as shown.

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4.6.1 EMBEDDING ALGORITHM

• This secret value is then embedded at predefined
  location within the Serial Number partition by
  appending it to the original Serial Number Value
  (SNorg) to generate the appended Serial Number
  (SNapp).

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4.6.2 EXTRACTION AND DETECTION ALGORITHM

• Extraction Stage
• The following parameters i.e. A, B and C are
  extracted from SNapp using the pattern P.
• Detection Stage
• In the detection stage the values of EM, OC and
  SNorg are hashed using the same one way function
  set F f1, f2, f3.
• These values are now compared with the extracted
  parameters to identify any data tampering.
• If the parameters match then we conclude that
  there is no tampering happened for EC, OC and
  SNorg.
• However, if the extracted parameters do not match
  then data tampering can be detected.

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4.6.3 EXTRACTION AND DETECTION ALGORITHM

• The actual source of data tampering can be
  identified based on the facts which are
  illustrated in the following Table.

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4.7 DISUCSSION

• The tamper detection technique that we presented
  is useful is identifying whether data tampering
  has happened and where the data is tampered. It
  is not a tamper proof solution.
• The most likely location where tampering would
  happen is the EPC Manager or the Object Class
  partition.
• This is because we assume that the motivation
  behind tampering would be to disguise a product
  against another for
• Cheaper shipping cost or
• Smuggling goods or
• Other economic benefits

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4.7.1 DISUCSSION

• The proposed technique offers a binary result
  i.e. it can tell that tampering has happened in
• EM or OC or SNapp
• But it is not possible to ascertain whether the
  tampering was in EM or in SNapp or in OC.
• However the mere fact that there is an
  inconsistency between EM and A or OC and B is
  enough to identify tampering.
• The other option that we explored was to have a
  copy of the SNapp in the backend database so that
  in case of tampering we can precisely tell what
  was tampered i.e. EM or OC or SNorg.
• However this is not feasible because the content
  in the RFID tag represents a type of composite
  key, and any tampering on the tag would make it
  difficult to uniquely identify the tag in the
  backend database. Hence a copy of SNapp in
  backend database does not offer any additional
  security.

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5. SUMMARY

• In this research, we identified some security
  issues in low cost RFID deployment.
• Focusing on the data tampering issue, we found
  the majority of recent research work in RFID
  security assumes the deployment of next
  generation RFID technology, which requires
  excessive computing capability and hence high
  cost in the RFID tag.
• We proposed a new data tamper detection framework
  by introducing a flexible layer into existing
  RFID middleware architecture.
• We also gave a detailed description of the data
  tampering algorithm which can detect and identify
  whether and what data is tampered on the RFID
  tags.

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Questions SuggestionsThank you for your
attention!
2005 International Conference on Computational
Intelligence and Security, Xian, China December
15-19, 2005