Investigation of Primary User Emulation Attack in Cognitive Radio Networks

1
Investigation of Primary User Emulation Attack in
Cognitive Radio Networks
Phd Proposal

• Chao Chen
• Department of Electrical Computer Engineering
• Stevens Institute of Technology
• Hoboken, NJ 07030

2
Outline

• Background
• Cognitive radio technology
• Security issues in cognitive radios
• Spectrum sensing in cognitive radios
• Primary user emulation attack
• Cooperative sensing in the presence of primary
  user emulation attack
• Cooperative sensing in the presence of PUEA with
  channel estimation error
• Cooperative sensing with multiple PUE attackers
• Cooperative Sensing with multiple antennas in the
  presence of PUEA
• Conclusion and future work

3
Background

• Wireless communication system design requires
  higher data rate and larger channel capacity as
  well as better quality of service and spectrum
  utilization efficiency to meet the needs of
  wireless users.
• Security issues have drawn much research
  attention in wireless communications due to its
  open air nature.

4
Cognitive Radio Technology

• Motivation
• 1. Frequency spectrum a scarce resource

Figure 1. Frequency allocation chart in US as of
2003
5
Cognitive Radio Technology

• Motivation
• 2. Spectrum access is a more significant problem
  than spectrum scarcity.

Figure 2. Measurements of spectrum utilization in
downtown Berkeley
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Cognitive Radio Technology

• Definition
• Cognitive radio 1 is a technology of wireless
• communications in which a network or a user
• flexibly changes its transmitting or receiving
• parameters to achieve more efficient
• communication performance without interfering
  with
• licensed or unlicensed users.
• 1. J. Mitola and G. Maguire, Cognitive radio
  Making software radios more
• personal, IEEE Communication Magazine, vol. 6,
  no. 4, pp. 1318, Aug. 1999.

7
Cognitive Radio Technology

• Spectrum holes

Figure 3. Illustration of spectrum holes
8
Cognitive Radio Technology

• Advances of cognitive radios
• J. Mitola
• I. Akyildiz
• S. Haykin
• Q. Zhao

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Cognitive Radio Technology

• Main functions

10
Cognitive Radio Technology

• Cognitive cycle

11
Security Issues in CR Networks

• Challenges
• The intrinsic properties of cognitive radio
  paradigm produce new threats and challenges to
  wireless communications 2.
• Spectrum occupancy failures Policy failures
  Location failures Sensor
• Failures Transmitter/Receiver failures
  Operating system disconnect
• Compromised cooperative CR Common control
  channel attacks.

2. T. Brown and A. Sethi, Potential cognitive
radio denial-of-service vulnerabilities and
protection countermeasures A multidimensional
analysis and assessment, IEEE International
Conference on Cognitive Radio Oriented Wireless
Networks and Communications (CrownCom), Aug.
2007, pp. 456-464.
12
Spectrum Sensing in Cognitive Radios

• Definition
• Spectrum sensing is to obtain awareness about
  the spectrum usage and existence of primary users
  in a geographical area.

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Spectrum Sensing in Cognitive Radios

• Spectrum opportunity

Figure 4. Multiple dimensional spectrum
opportunity
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Spectrum Sensing in Cognitive Radios

• Spectrum sensing
• A classical signal detection problem

channel gain
noise
primary signal
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Spectrum Sensing in Cognitive Radios

• Spectrum sensing methods

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Spectrum Sensing in Cognitive Radios

• Transmitter detection
• 1) Matched filter detection
• Advantages Better detection performance and less
  time to achieve
• processing gain
• Disadvantages Priori knowledge of primary signal
  is required (such as
  pilots, preambles or synchronized messages).

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Spectrum Sensing in Cognitive Radios

• Transmitter detection
• 2) Energy detection
• Decision statistic Y follows Chi-square
  distribution

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Spectrum Sensing in Cognitive Radios

• Transmitter detection
• 2) Energy detection
• False alarm probability and detection
  probability
• is decision threshold

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Spectrum Sensing in Cognitive Radios

• Transmitter detection
• 3) Cyclostationary detection
• Exploits built-in periodicity of modulated
  signals
• couple with sine wave carriers, hopping
  sequences,
• cyclic prefixes and etc.
• Advantages better performance than energy
  detection
• Disadvantages more computational complexity and
  
• longer observation time.

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Spectrum Sensing in Cognitive Radios

• Cooperative detection

Figure 5. Transmitter detection problem
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Spectrum Sensing in Cognitive Radios

• Cooperative detection

Figure 6. Cooperative detection model
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Spectrum Sensing in Cognitive Radios

• Cooperative detection
• Fusion rules
• Hard combination (1 bit) AND rule, OR rule,
  majority
• rule
  
• Soft combination (n bits) soft sensing
  information
• (e.g., signal energy) 3.
• 3. J. Ma, G. Zhao, and Y. Li, Soft combination
  and detection for cooperative spectrum
• sensing in cognitive radio networks, IEEE
  Transactions on Wireless
• Communications, vol. 7, no. 11, pp. 4502
  4507, Nov. 2008.

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Spectrum Sensing in Cognitive Radios

• Interference temperature detection

Figure 7. Interference temperature detection
24
Spectrum Sensing in Cognitive Radios

• Challenges
• Hardware requirement
• Hidden primary user problem
• Primary users detection in spread spectrum
• Detection capability
• Decision fusion in cooperative detection
• Security issues

25
Primary User Emulation Attack

• Definition
• An attacker occupies the unused channels
• by emitting a signal with similar form as the
• primary users signal so as to prevent other
• secondary users from accessing the vacant
• frequency bands 4.

4. R. Chen, J. Park, and J. Reed, Defense
against primary user emulation attacks in
cognitive radio networks, IEEE Journal on
Selected Areas in Communications, vol. 26, no. 1,
pp. 2537, Jan. 2008.
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Primary User Emulation Attack

• Detection of PUEA
• Distance ratio test distance difference test
  
• Walds sequential probability ratio test

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Primary User Emulation Attack

• Defense against PUEA
• Localization based transmitter verification
• procedure
• Channel identification
• Dogfight and blind dogfight

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Cooperative Spectrum Sensing in thePresence of
PUEA

• System model

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Cooperative Spectrum Sensing in thePresence of
PUEA

• System model
• The signal received by the ith secondary user
• at the kth time instant is

primary users signal with power Pp
attackers signal with power Pm
channel gain between primary and ith secondary
user
channel gain between attacker and ith secondary
user
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Cooperative Spectrum Sensing in thePresence of
PUEA

• System model
• The combined signal in the fusion center at the
• kth time instant is,

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Cooperative Spectrum Sensing in thePresence of
PUEA

• System model
• When there is a PUEA, i.e., ß 1, the detection
• problem is reformulated as,
• After energy detector,

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Cooperative Spectrum Sensing in thePresence of
PUEA

• Optimal combining scheme
• Objective To design optimal weights to
• maximize the detection probability under the
• constraint of a prefixed false alarm probability
• where

33
Cooperative Spectrum Sensing in thePresence of
PUEA

• Optimal combining scheme
• Assumption Block fading k is omitted in
  and
• For given and , the combined signal
  is also a
• complex Gaussian distributed random variable,
• where,

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Cooperative Spectrum Sensing in thePresence of
PUEA

• Optimal combining scheme
• Decision statistic Y is compliant with central
  chi
• square distribution for both H0 and H1,
• And Pd and Pf are expressed as,

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Cooperative Spectrum Sensing in thePresence of
PUEA

• Optimal combining scheme
• Optimization objective
• where

Quadratic form
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Cooperative Spectrum Sensing in thePresence of
PUEA

• Optimal combining scheme
• Optimal solution

is the largest eigenvalue of
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Cooperative Spectrum Sensing in thePresence of
PUEA

• Optimal combining scheme
• Remarks
• 1) if Pm 0,
• 
  
• 2) virtual antenna array
• 3) average detection probability over fading
  
• channel

MRC
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Cooperative Spectrum Sensing in thePresence of
PUEA

• Optimal combining scheme
• Remarks
• 4) acquisition of channel information
• a. priori knowledge such as pilots,
• synchronization messages, preambles...
• b. blind channel estimation

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Cooperative Spectrum Sensing in thePresence of
PUEA

• Simulation results

(b) N 4
(a) N 2
N is the number of secondary user
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Cooperative Spectrum Sensing in thePresence of
PUEA

• Simulation results

(c) N 6
(d) N 8
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Cooperative Spectrum Sensing in thePresence of
PUEA

• Simulation results

42
Cooperative Spectrum Sensing in thePresence of
PUEA

• Different network scenarios of PUEA for two users
  case

43
Cooperative Spectrum Sensing in thePresence of
PUEA

• Simulation results

44
Cooperative Spectrum Sensing in the Presence of
PUEA with Channel Estimation Error

• System model

estimation error
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Cooperative Spectrum Sensing in the Presence of
PUEA with Channel Estimation Error

• System model

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Cooperative Spectrum Sensing in the Presence of
PUEA with Channel Estimation Error

• Average detection probability

47
Cooperative Spectrum Sensing in the Presence of
PUEA with Channel Estimation Error

• Simulation results

48
Cooperative Spectrum Sensing in the Presence of
PUEA with Channel Estimation Error

• Simulation results

49
Cooperative Spectrum Sensing in the Presence of
PUEA with Channel Estimation Error

• Simulation results

50
Cooperative Spectrum Sensing in the Presence of
PUEA with Channel Estimation Error

• Simulation results

51
Cooperative Spectrum Sensing in the Presence of
Multiple PUE Attackers

• System model

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Cooperative Spectrum Sensing in the Presence of
Multiple PUE Attackers

• System model
• The signal received by the ith secondary user
• at the kth time instant is

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Cooperative Spectrum Sensing in the Presence of
Multiple PUE Attackers

• Optimal weights

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Cooperative Spectrum Sensing in the Presence of
Multiple PUE Attackers

• Simulation results

(a) K 2
(a) K 4
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Cooperative Spectrum Sensing in the Presence of
Multiple PUE Attackers

• Simulation results

(c) K 6
(d) K 8
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Cooperative Spectrum Sensing in the Presence of
Multiple PUE Attackers

• Normalized attacking power

57
Cooperative Spectrum Sensing withMultiple
Antennas in the Presence of PUEA

• Multiple antenna technology

58
Cooperative Spectrum Sensing withMultiple
Antennas in the Presence of PUEA

• System model

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Cooperative Spectrum Sensing withMultiple
Antennas in the Presence of PUEA

• System model
• The received signal at ith user at the kth
  detection
• instant is,
• the final combined signal at the fusion center
• is given as,

60
Cooperative Spectrum Sensing withMultiple
Antennas in the Presence of PUEA
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Cooperative Spectrum Sensing withMultiple
Antennas in the Presence of PUEA

• Simulation results

(a) 2 antenna case
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Cooperative Spectrum Sensing withMultiple
Antennas in the Presence of PUEA

• Simulation results

(b) 3 antenna case
63
Cooperative Spectrum Sensing withMultiple
Antennas in the Presence of PUEA

• Simulation results

(c) 4 antenna case
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Conclusion

• Conclusion
• We have studied the cooperative spectrum
  sensing in CR network in the presence of primary
  user emulation attack. Through the proposed
  optimal combination scheme, the detection
  probability of the spectrum hole is optimized
  under the constraint of a required false alarm
  probability. Simulation results show the
  detection performance improvement of the proposed
  optimal combining scheme over the conventional
  MRC method.

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Conclusion

• Conclusion
• Investigation of the detection performance when
  the channel estimation error is considered in the
  proposed scheme.
• Investigation of the detection performance when
  multiple PUE attackers are considered in the
  network scenario.

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Publication

• Chao Chen, Hongbing Cheng, Yu-Dong Yao,
  Cooperative Spectrum Sensing in the Presence of
  Primary User Emulation Attack in Cognitive Radio
  Networks, under 2nd round review of IEEE
  transactions on wireless communications.

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Thank you!