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Investigation of Primary User Emulation Attack in Cognitive Radio Networks

This PhD proposal delves into the security issues of cognitive radios, focusing on the primary user emulation attack. It covers spectrum sensing, cooperative sensing, and challenges in the presence of PUEA, with the aim of enhancing network security.

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Investigation of Primary User Emulation Attack in Cognitive Radio Networks

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  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

  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

  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. 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

  9. 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.

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

  14. Spectrum Sensing in Cognitive Radios • Spectrum sensing —— A classical signal detection problem channel gain noise primary signal

  15. Spectrum Sensing in Cognitive Radios • Spectrum sensing methods

  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).

  17. Spectrum Sensing in Cognitive Radios • Transmitter detection 2) Energy detection Decision statistic Y follows Chi-square distribution

  18. Spectrum Sensing in Cognitive Radios • Transmitter detection 2) Energy detection False alarm probability and detection probability is decision threshold

  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.

  20. Spectrum Sensing in Cognitive Radios • Cooperative detection Figure 5. Transmitter detection problem

  21. Spectrum Sensing in Cognitive Radios Cooperative detection Figure 6. Cooperative detection model

  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. 4502 – 4507, Nov. 2008.

  23. 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 byemitting 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. 2008.

  26. Primary User Emulation Attack • Detection of PUEA • Distance ratio test & distance difference test • Wald’s sequential probability ratio test

  27. Primary User Emulation Attack • Defense against PUEA • Localization basedtransmitter verification procedure • Channel identification • Dogfight and blind dogfight

  28. Cooperative Spectrum Sensing in thePresence of PUEA • System model

  29. Cooperative Spectrum Sensing in thePresence 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

  30. Cooperative Spectrum Sensing in thePresence of PUEA • System model The combined signal in the fusion center at the kth time instant is,

  31. Cooperative Spectrum Sensing in thePresence of PUEA • System model When there is a PUEA, i.e., β = 1, the detection problem is reformulated as, After energy detector,

  32. 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,

  34. 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 Pfare expressed as,

  35. Cooperative Spectrum Sensing in thePresence of PUEA • Optimal combining scheme Optimization objective: where Quadratic form

  36. Cooperative Spectrum Sensing in thePresence of PUEA • Optimal combining scheme Optimalsolution: is the largest eigenvalue of

  37. 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

  38. 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

  39. Cooperative Spectrum Sensing in thePresence of PUEA • Simulation results (b) N = 4 (a) N = 2 N is the number of secondary user

  40. Cooperative Spectrum Sensing in thePresence of PUEA • Simulation results (c) N = 6 (d) N = 8

  41. 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

  45. Cooperative Spectrum Sensing in the Presence of PUEA with Channel Estimation Error • System model

  46. 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

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