1. Phd Proposal
Investigation of Primary User Emulation Attack in Cognitive Radio Networks
Chao Chen
Department of Electrical & Computer Engineering
Stevens Institute of Technology
Hoboken, NJ 07030
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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
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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.
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4. Cognitive Radio Technology
Motivation
1. Frequency spectrum —— a scarce resource
Primary user Secondary user
Figure 1. Frequency allocation chart in US as of 2003
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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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6. 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. 13–18, Aug
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7. Cognitive Radio Technology
Spectrum holes
Figure 3. Illustration of spectrum holes
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8. Cognitive Radio Technology
Advances of cognitive radios
J. Mitola
I. Akyildiz
S. Haykin
Q. Zhao
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9. Cognitive Radio Technology
Main functions
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10. Cognitive Radio Technology
Cognitive cycle
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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
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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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13. Spectrum Sensing in Cognitive Radios
Spectrum opportunity
Figure 4. Multiple dimensional spectrum opportunity
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14. Spectrum Sensing in Cognitive Radios
A classical signal detection problem
channel gain
noise
primary signal
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15. Spectrum Sensing in Cognitive Radios
Spectrum sensing methods
Spectrum sensing
Transmitter detection
Matched filter detection
Energy detection
Cyclostationary
detection
Cooperative detection
Interference temperature detection
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16. 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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17. Spectrum Sensing in Cognitive Radios
Transmitter detection
2) Energy detection
Decision statistic Y follows Chi-square distribution
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18. Spectrum Sensing in Cognitive Radios
Transmitter detection
2) Energy detection
False alarm probability and detection probability
is decision threshold
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19. 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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20. Spectrum Sensing in Cognitive Radios
Cooperative detection
Figure 5. Transmitter detection problem
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21. Spectrum Sensing in Cognitive Radios
Cooperative detection
Figure 6. Cooperative detection model
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22. 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 – 4507, Nov
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23. Spectrum Sensing in Cognitive Radios
Interference temperature detection
Figure 7. Interference temperature detection
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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
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25. Primary User Emulation Attack
Definition
An attacker occupies the unused channels
by emitting a signal with similar form as the
primary user’s 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. 25–37, Jan
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26. Primary User Emulation Attack
Detection of PUEA
Distance ratio test & distance difference test
Wald’s sequential probability ratio test
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27. Primary User Emulation Attack
Defense against PUEA
Localization based transmitter verification
procedure
Channel identification
Dogfight and blind dogfight
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28. Cooperative Spectrum Sensing in the Presence of PUEA
System model
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29. Cooperative Spectrum Sensing in the Presence of PUEA
System model
The signal received by the ith secondary user
at the kth time instant is
: primary user’s signal with power Pp
: attacker’s signal with power Pm
: channel gain between primary and ith secondary user
: channel gain between attacker and ith secondary user
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30. Cooperative Spectrum Sensing in the Presence of PUEA
System model
The combined signal in the fusion center at the
kth time instant is,
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31. Cooperative Spectrum Sensing in the Presence of PUEA
System model
When there is a PUEA, i.e., β = 1, the detection
problem is reformulated as,
After energy detector,
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32. Cooperative Spectrum Sensing in the Presence 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
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33. Cooperative Spectrum Sensing in the Presence 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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34. Cooperative Spectrum Sensing in the Presence 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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35. Cooperative Spectrum Sensing in the Presence of PUEA
Optimal combining scheme
Optimization objective:
where
Quadratic form
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36. Cooperative Spectrum Sensing in the Presence of PUEA
Optimal combining scheme
Optimal solution:
is the largest eigenvalue of
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37. Cooperative Spectrum Sensing in the Presence 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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38. Cooperative Spectrum Sensing in the Presence 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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39. Cooperative Spectrum Sensing in the Presence of PUEA
Simulation results
(b) N = 4
(a) N = 2
N is the number of secondary user
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40. Cooperative Spectrum Sensing in the Presence of PUEA
Simulation results
(c) N = 6
(d) N = 8
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41. Cooperative Spectrum Sensing in the Presence of PUEA
Simulation results
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42. Cooperative Spectrum Sensing in the Presence of PUEA
Different network scenarios of PUEA for two users case
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43. Cooperative Spectrum Sensing in the Presence of PUEA
Simulation results
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44. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error
System model
estimation error
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45. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error
System model
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46. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error
Average detection probability
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47. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error
Simulation results
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48. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error
Simulation results
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49. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error
Simulation results
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50. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error
Simulation results
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51. Cooperative Spectrum Sensing in the Presence of Multiple PUE Attackers
System model
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52. 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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53. Cooperative Spectrum Sensing in the Presence of Multiple PUE Attackers
Optimal weights
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54. Cooperative Spectrum Sensing in the Presence of Multiple PUE Attackers
Simulation results
(a) K = 2
(a) K = 4
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55. Cooperative Spectrum Sensing in the Presence of Multiple PUE Attackers
Simulation results
(c) K = 6
(d) K = 8
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56. Cooperative Spectrum Sensing in the Presence of Multiple PUE Attackers
Normalized attacking power
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57. Cooperative Spectrum Sensing with Multiple Antennas in the Presence of PUEA
Multiple antenna technology
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58. Cooperative Spectrum Sensing with Multiple Antennas in the Presence of PUEA
System model
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59. Cooperative Spectrum Sensing with Multiple 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,
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60. Cooperative Spectrum Sensing with Multiple Antennas in the Presence of PUEA
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61. Cooperative Spectrum Sensing with Multiple Antennas in the Presence of PUEA
Simulation results
(a) 2 antenna case
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62. Cooperative Spectrum Sensing with Multiple Antennas in the Presence of PUEA
Simulation results
(b) 3 antenna case
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63. Cooperative Spectrum Sensing with Multiple Antennas in the Presence of PUEA
Simulation results
(c) 4 antenna case
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64. 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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65. 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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66. 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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67. Thank you!
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