Wireless Communications Security Issues, Solutions and Challenges

1
Wireless Communications Security Issues,
Solutions and Challenges

• Michel Barbeau and Jeyanthi Hall

2
Outline

• Availability
• Privacy
• Integrity
• Legitimate Participants
• Absence of misbehavior

3
Security Requirements

• Availability
• no jamming, adaptability to unforeseen topologies
• Privacy
• nondisclosure of cell phone communications and
  802.11 frames
• Integrity
• data is not intercepted and tampered
• Legitimate participants
• no cell phone cloning and 802.11 frame spoofing
• Absence of misbehavior
• fairness, greedy user detection

4
Availability

• Jamming
• Inability to deal with unforeseen topologies

5
Jamming

• Shannons model

6
How to Deal With Jamming?

• Increase the bandwidth
• Frequency Hopping/Direct Sequence Spread Spectrum
• 801.11(b) 2.4 - 2.4835 Giga Hertz
• 801.11(a) 5.15- 5.35 Giga Hertz 5.725- 5.825
  Giga Hertz
• Ultra Wide Band
• Bandwidth greater than 25 if center frequency
• Increase the power
• GPS III, planned for 2010 Ashley,
  Next-Generation GPS, Scientific American,
  September 2003.

7
Inability to Deal With Unforeseen Topologies
Images by J.G. Naudet (9/11/2001)
8
Privacy

• Cellular phone eavesdropping
• Overview of privacy techniques in 2G and 3G of
  cellular mobile radiophones
• Refs.
• V. Niemi and K. Nyberg, UMTS Security, Wiley,
  2003.
• M.Y. Rhee, CDMA Cellular Mobile Communications
  and Network Security, Prentice Hall PTR,1998.
• GSM, UMTS
• Challenges
• Future
• Reconfigurable security
• Chaotic communication
• Quantum cryptography

9
Cellular Phone Eavesdropping

• Inexpensive equipment for intercepting analog
  communications is easy to obtain in Canada.
• In US, the regulations authorize the sale of
  scanners to the general public only is cellular
  frequencies are blocked. However, there are
  several workarounds
• Web sites publish modifications to restore
  reception of cellular frequencies by scanners.
• Frequency converters can translate cellular
  frequencies to the frequency range supported by a
  receiver.
• With receivers using non quadrature mixing, the
  image frequency technique can be used.
• Digital communications can also be intercepted
  with the appropriate equipment!

10
Generations of Cellular Mobile Radiophones

• 1G
• Advanced Mobile Phone System (AMPS) 1980s,
  Frequency Modulation (FM), Frequency Division
  Multiple Access (FDMA), handover between cells,
  limited roaming between networks
• 2G
• Global System for Mobile communications (GSM)
  1990s, digital-coding of voice, Time Division
  Multiple Access (TDMA), Subscriber Identity
  Module (SIM), data communications
• 3G
• 3G Partnership Project (3GPP), Universal Mobile
  Telecommunications System (UMTS) 1998-, Wideband
  Code Division Multiple Access (WCDMA), use of GSM
  network model, global roaming 2 Mbps data
• 4G
• All-IP-based, 100 Mbps data

List of cited technologies is not exhaustive.
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Security Associations in GSM
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Authentication in GSM
RAND Random Number SRES Signed Response
13
Encryption/Decryption in GSM
14
Stream Cipher Weakness
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Security Holes in GSM Niemi Nyberg 03

• Active attack
• Attacker masquerades as a legitimate base
  station/cell phone
• Encryption keys
• Plain text session key inter-network forwarding
• Brute force attack
• Some encryption algorithms are kept secret
• Were not subjected to a comprehensive
  analysis/peer review

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Security Associations in UMTS
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Mutual Authentication and Key Agreement in UMTS
AUTN Authentication Token RES User Response XRES
Expected Response
18
Encryption/Decryption in UMTS
COUNT-C Frame number plus Hyper frame number,
incremented when the frame number wraps
around Direction up/down-link
19
Integrity in UMTS
COUNT-I similar to COUNT-C, replay
protection FRESH start value of COUNT-I
20
Challenge Co-existence of analog technology and
digital technology

• The digital technology has higher potential for
  being secure than analog technology. For example,
  the Cellular Digital Packet Data (CDPD) uses data
  encryption and provides privacy.
• Most of the cellular phones use hybrid
  technology, both analog and digital. The reason
  for that is that digital communications require a
  relatively stronger signal, for intelligibility,
  than analog communications, all other things
  being equal (such as bandwidth of a voice
  channel). A cell phone will hence operate in
  digital mode over relatively short distances.
• In order to enable long range communications,
  cell phones fall back to the analog mode when the
  signal gets too weak for digital communications.
  As a result, digital systems inherit all the
  security vulnerabilities of analog systems.
• Co-existence of legacy analog technology and
  digital technology is a challenge for system
  security design.

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Challenge Introduction of new defense method in
existing systems

• Attack methods evolve
• Defense methods evolve
• New defense methods are difficult to introduce in
  existing systems

22
Reconfigurable security

• Reference
• Al-Muhtadi at al., A lightweight reconfigurable
  security mechanism for 3G/4G mobile devices, IEEE
  Wireless Communications, April 2002.
• Definition
• Security mechanisms are reconfigured dynamically
  according to capabilities, processing power, and
  needs
• Loading/configuration/unloading of software
  components that implement security services

23
Chaotic Communication (1)
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Chaotic Communication (2)

• Background
• Abel and Schwarz, Chaos CommunicationsPrinciples,
  Schemes, and System Analysis, Proceedings of the
  IEEE, 2002.
• Itoh, Spread Spectrum Communication via Chaos,
  World Scientific Publishing Company,
  International Journal of Bifurcation and Chaos,
  1999.
• Theoretical Attacks
• Guojie, Zhengjin, and Ruiling, Chosen Ciphertext
  Attack on Chaos Communication Based on Chaotic
  Synchronization, IEEE Transactions on Circuits
  and Systems, 2003.
• Ogorzatek and Dedieu, Some Tools for Attacking
  Secure Communication Systems Employing Chaotic
  Carriers, IEEE, 1998.

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Theoretically Broken Chaotic Communication
(contd)

• Chaotic masking
• Low amplitude modulating signal, high amplitude
  chaotic carrier
• Chaotic switching
• Two waveforms representing binary values zero and
  one
• Has a differential version
• Chaotic modulation
• Chaotic carrier influenced by a non invertible
  function, according to the information

26
Quantum Cryptography

• Wiesner, Quantum Money, 1960 (unpublished)
• Polarity of photons (angle of vibration) can be
  verified, but not measured
• Bennett, Brassard, and Ekert, Quantum
  Cryptography, Scientific American, October 1992.
• Hughes et al., Quantum cryptography for secure
  satellite communications, Aerospace Conference
  Proceedings, 2000.
• 0.5 km free-space link
• Kurtsiefer et al., Long Distance Free Space
  Quantum Cryptography, SPIE, 2002.
• 23.4 km free-space link (try to achieve 1000 km)
• First Quantum Cryptography Network Unveiled,
  NewScientist.com news service, June 2004.
• Quantum Net six servers, 10 km links,
  software-controlled optical switches

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Legitimate Devices

• PROBLEM
• AUTHENTICATION OF USERS IS INSUFFICIENT DUE TO
  MALLEABILITY OF USER IDENTITY

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Need for Device Authentication

• Outline
• Problem User Authentication is incapable of
  detecting identity theft
• Malleability of user identity
• Result
• Unauthorized access to network resources
• Within cellular domain (cloning fraud) and
  wireless network domain (Media Access Control
  MAC address spoofing)

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Wireless Network (e.g. 802.11)

• MAC address spoofing (over the air)

Wired Network
List of Authorized MAC Addresses (Access Control)
1
MAC Address
3
MAC Address
2
Intruder Sniff MAC Address and use it
Legitimate User
MAC address is sent in the clear even with WEP
Arbaugh et al., 2002
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Wireless Network (e.g. 802.11)

• With 802.11i standard uses 802.1x Extensible
  Authentication Protocol Mishra and Arbough,
  2002
• Absence of authentication of access point by
  device
• Man-in-Middle attack using ()
• Session Hijacking using ()

MAC address of access point and supplicant
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Cellular Network - Identification of 1G Cell Phone

• Every cellular phone is assigned,
• by the service provider, a phone number (Mobile
  station Identification Number (MIN))
• 10 digits area code (3), switching station (3),
  and individual number (4)
• by the manufacturer, an Electronic Serial Number
  (ESN)

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Identification of 2G or 3G Cell Phones Koien,
2004
According to ITU-T Recommendation E.212
International Mobile Station Equipment Identity
(IMEI) - Check against the Equipment Identity
Register
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Types of Cellular Phone Fraud

• Cellular theft
• Stolen phone is used by thief until theft is
  reported to the service provider who blocks the
  number and adds IMEI to the EIR
• Countermeasures PINs and biometrics Schiller,
  2000
• Subscription fraud
• A subscription with a cellular phone provider is
  obtained using false or stolen pieces of
  identification
• Tumbling fraud
• Cellular phone service providers grant automatic
  access for the first call to every visitor
  subscriber

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Cellular Network

• Cloning fraud

• 1 J. Hynninen, 2000
• 2 I. Goldberg and M. Briceno, 2002
• With a smartcard reader, derive the secret key by
  challenging the SIM-card (approx. 150,000
  queries eight to 11 hours)
• 3 R.Lemos, 2002
• Ask seven questions and analyze electromagnetic
  field changes and power fluctuations for each
  response

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User Authentication in GSM
SIM
RAND Random Number SRES Signed Response SIM
Subscriber Identity Module (IMSI, AuthKey Ki,
CipherKey Kc, Algorithms, PIN)
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References

• Wireless Network
• Arbaugh et al. Your 802.11 Wireless Network has
  no clothes, IEEE Wireless Communications. Dec.
  2002.
• Mishra and Arbough. An Initial Security Analysis
  of the IEEE 802.1X Standard. 2002.
• Cellular Network
• G. Koien et al. An Introduction to Access
  Security in UMTS, IEEE Wireless Communications.
  Feb. 2004.
• I. Goldberg and M. Briceno. GSM Cloning. 2002
  Web.
• J. Hynninen. Experiences in Mobile Phone fraud.
  Helsinki University of Technology Web.
• R.Lemos. IBM Cell phones easy targets for
  hackers. CNET News. 2002.
• Others
• J. Schiller. Mobile Communications.
  Addison-Wesley. 2000.

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Radio Frequency Fingerprinting

• Mechanism for addressing the malleability of user
  identity

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Radio Frequency Fingerprinting (RFF)

• Background
• Technique used by research teams including H.
  Choe et al., 1995, Ureten 1999 for the purpose
  of identifying RF transceivers
• Premise a transceiver can be uniquely
  identified based on the characteristics of the
  transient section of the signal it generates
• Primary benefit Non-malleability of device
  identity
• based on hardware characteristics of the
  transceiver
• Key Objective
• Create a profile of the users device
  (transceiver) using RFF
• Make use of both user and device profiles for
  authentication purposes
• Wireless Network device profile and MAC address
• Cellular Network device profile and IMSI

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RFF

• Key Phases
• Create profile for each transceiver
• Phase 1 Collection of Signals
• Phase 2 Extraction of Transient
• Phase 3 Extraction of Features
  (transceiverprint - TP)
• Phase 4 Definition of Transceiver Profile
• Classify/Compare an observed TP with transceiver
  profiles
• Phase 1-3 Repeated for each observed TP
• Phase 5 Identification of transceiver
• Improve Classification Success Rate (CSR)
  Proposed Extension to RFF process
• Phase 6 Enhancement of CSR (work in progress)

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RFF Phase 1 - Collect Signals
GSM Protocol Stack
802.11 Protocol Stack Schiller, 2000
CM
TCP
MM
IP
RR
LLC
LAPDm TDMA Frame
MAC - Frame
Radio - Burst
PHY FHSS/DSSS Frame
Layer 1
Analog Signal transmitted by physical layer 1
frame Authentication Response more than 1
frame/signal
CM Call Management MM Mobility Management RR
Radio Resource Management LAPD Link Access
Procedure for D-Channel in ISDN
system
LLC Logical Link Control FHSS Frequency
Hopping Spread Spectrum DSSS Direct Sequence
Spread Spectrum
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RFF Phase 1 - Collect Signals

• Capture analog signals from each transceiver and
  convert it to a digital format using an ADC
• View/Analyze digital signal in the time,
  frequency, phase domain

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RFF Phase 2 Extraction of Transient

• Extract transient section of digital signal
• Step 1 Preprocessing
• Segmenting the signal and applying first-order
  statistics (data reduction exercise)
• Results in a smaller vector data/fractal
  trajectory
• Step 2 Detection of the start of the transient
  using data trajectory
• Using the variance in the amplitude
  characteristics of the signal
• Threshold Detection
• Bayesian Step Change Detection
• Using the variance in the phase characteristics
  of the signal
• Threshold Detection using Phase Characteristics

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RFF Phase 2 Extraction of Transient

• Threshold Detection Shaw and Kinsner, 1997

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RFF Phase 2 Extraction of Transient

• Bayesian Step Change Detection Ureten, 1999

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RFF Phase 2 Extraction of Transient

• Threshold Detection using Phase Characteristics
  Hall, Barbeau, Kranakis (IASTED, 2003)

demo
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RFF Phase 3 Extraction of Components

• Extract components/characteristics from the
  transient
• Instantaneous amplitude Proakis and Manolakis,
  1996
• Instantaneous phase
• Instantaneous frequency components Polikar,
  1999
• using Discrete Wavelet Transform (Daubechies
  filter)
• Wavelet function
• Scaling function

47
RFF Phase 3 Extraction of Components
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RFF Phase 3 Extraction of Features

• Extract features from components (vector of 1000
  samples)
• Average, Standard Deviation, Energy, Variance
• Representation of features (dependent on
  classification tool)
• Challenge/Goal
• Select features (transceiverprint) that
  accentuate the distinguishing characteristics of
  transceivers, especially those from the same
  manufacturer

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RFF Phase 4 Definition of Profile

• Create profile for each transceiver
• Obtain TPs from each signal in the collected data
  set (Phases 2-3)
• Select a subset of TPs and store them in a
  profile (remaining TPs used for
  testing/classification)
• Using Self-Organizing Maps Fausett, 1994
• Take TPs from the data set as input
• Create group(s) / cluster(s) of transceiverprints
  based on their distance (Euclidean distance) from
  a given centroid
• Select a representative sample of TPs from the
  various clusters to create a profile
• Other approaches include
• Random selection of TPs from the data set
• Use of probabilistic neural network Hunter, 2000

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RFF Phase 5 Identification of transceiver

• Classification Techniques
• Pattern matching e.g. Neural Networks
  (Artificial NN, Probabilistic NN, etc.) Fausett,
  1994
• Based on Bayes Probabilistic Model
• Genetic Algorithms Toonstra and Kinsner, 1995
• Achieve an optimized solution through multiple
  iterations
• Statistical classifiers Brickle, 2003
• Determine probability of a match between an
  observed transceiverprint (TP) and each of the
  transceiver profiles

TP to be classified centroid center of
cluster covariance matrix of TPs in profile
Modified Kalman Filter
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RFF Phase 6 Enhancement of CSR

• Weakness in current classification techniques
• attempt to identify transceiver using a single
  observation (TP)
• unable to accommodate moderate level of variation
  (interference and noise) in the TPs being
  classified
• Address weakness using the Bayes Filter Fox et
  al., 2003
• Identify transceiver with highest probability
  after several rounds (using consecutive TPs) of
  classification

xt Transceiver at time t Bel(xt) Probability
of Transceiver x at time t
Bel(xt) p(xtot)Bel(xt-1)
p(xt ot) Probability of TP belonging to
transceiver x at time t Bel(xt-1) Probability
of transceiver x at t-1
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RFF Phase 6 Enhancement of CSR
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Conclusions

• Use of RFF can prove beneficial in addressing
  malleability of identity (MAC address spoofing,
  cloning fraud)
• Level of confidence can be increased by using the
  Bayes Filter before rendering a final decision
  (legitimate user/intruder)
• The issue of scalability can be addressed
• Application of Bayes filter to the target
  transceiver profile only for transceiver
  recognition/confirmation
• Based on the final probability, Bayes filter can
  then be applied to identify other potential
  transceivers
• Future Research Initiatives
• Enhancing the composition of TPs improve
  classification rate
• Using RFF with Bluetooth and cellular phones
• Assessing the technical feasibility of
  incorporating RFF into current security systems

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References

• Radio Frequency Fingerprinting
• Amplitude
• O. Ureten and N. Serinken. Detection of radio
  transmitter turn-on transients. Electronic
  Letters, 3519961997, 1999.
• D. Shaw and W. Kinsner, Multifractal Modeling of
  Radio Transmitter Transients for Classification,
  Proc. Conference on Communications, Power and
  Computing, 1997, 306-312.
• Phase
• J. Hall, M. Barbeau, E. Kranakis. Detection of
  transient in radio frequency fingerprinting using
  phase characteristics of signals. In L.Hesslink
  (Ed.), Proceedings of the 3rd International
  IASTED Conference on Wireless and Optical
  Communication, Banff, Canada, 13-18, 2003.
• Wavelet Coefficients
• H. Choe et al. Novel identification of
  intercepted signals from unknown radio
  transmitters. SPIE, 2491504516, 1995.
• R.D. Hippenstiel and Y.P. Wavelet based
  transmitter identification. In International
  Symposium on Signal Processing and its
  Applications, Gold Coast Australia, August 1996.

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References

• Bayes Filter
• D. Fox et al. Bayesian Filtering for location
  estimation. Pervasive Computing. 24-33, 2003.
  
• Statistical Classifier
• Frank Brickle. Automatic signal classification
  for software defined radios. QEX, pages 3441,
  November 2003.
• Others
• A. Hunter. Feature Selection using Probabilistic
  Neural Networks. Neural Computing and
  Applications. 124-132, 2000.
• J. Schiller. Mobile Communications.
  Addison-Wesley, 2000.
• J. Proakis and D. Manolakis. Digital Signal
  Processing. Prentice-Hall, 1996.
• J. Toonstra and W. Kinsner. Transient Analysis
  and Genetic Algorithms for Classification. IEEE
  WESCANEX 95. 432-437, 1995
• L. Fausett. Fundamentals of Neural Networks.
  Prentice-Hall, 1994.
• R. Polikar. The Wavelet Tutorial. web

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Thank You

• Michel Barbeau (barbeau_at_scs.carleton.ca)
• Jeyanthi Hall (jeyanthihall_at_rogers.com)

Questions ?