Mitigation of Primary User Emulation Attack using Time of Emission Estimation

1. Mitigation of Primary User Emulation Attack using Time of Emission Estimation NatrajJaganmohan (njaganm) Sandeep A Rao (sarao) CSC774 - NCSU ADVANCED NETWORK SECURITY

2. Agenda of the presentation: • Background about Cognitive Radio Networks • Primary User Emulation Attack (PUEA) • Existing approaches to solve PUEA. • PUEA attack model with Directional antennas. • Attack mitigation using TOE estimation. • Simulation results. • Limitations of the approach. • Future directions of research. CSC774 - NCSU ADVANCED NETWORK SECURITY

3. It all started here: “All consumers . . . deserve a new spectrum policy paradigm that is rooted in modern-day technologies and markets. We are living in a world where demand for spectrum is driven by an explosion of wireless technology and the ever-increasing popularity of wireless services. Nevertheless, we are still living under a spectrum 'management' regime that is 90 years old. It needs a hard look, and in my opinion, a new direction.” Michael K. Powell (Chairman FCC Spectrum Policy Task Force) CSC774 - NCSU ADVANCED NETWORK SECURITY

4. Spectrum Scarcity: • Cognitive Networks help us solve the problem. CSC774 - NCSU ADVANCED NETWORK SECURITY

5. Background: Cognitive Radio Networks. • Wireless spectrum is very scarce leading to spectrum crisis. • FCC recommends use of opportunistic or cognitive networks to increase spectrum utilization. • This technology would put unused and under-used spectrum assets to work – without impacting  primary users within those bands. It is a bold, yet workable solution. CSC774 ADVANCED NETWORK SECURITY

6. Background: Cognitive Radio Networks. • “A Cognitive Radio is a radio frequency transmitter/receiver that is designed to intelligently detect whether a particular segment of the radio spectrum is currently in use, and to jump into (and out of, as necessary) the temporarily-unused spectrum very rapidly, without interfering with the transmissions of other authorized users.” • http://www.ieeeusa.org/forum/POSITIONS/cognitiveradio.html CSC774 - NCSU ADVANCED NETWORK SECURITY

7. Cognitive Radio networks operation: PU-Tx PU-RX PU-RX SU SU PU-RX CSC774 - NCSU ADVANCED NETWORK SECURITY

8. What makes Cognitive Networks possible? Key enablers of CRNs: • Radio manufacturers have started to create flexible software-defined radios. • Research funding and support for spectrum re-use. • Support for Dynamic Channel selection, channel scanning and adjustable transmission power. CSC774 - NCSU ADVANCED NETWORK SECURITY

9. Some terminologies used in this presentation: • CRN: Cognitive Radio Network • PU: Primary User (licensed user) • SU: Secondary user (CRN node) • PUEA: Primary User Emulation Attack • FC: Fusion Center • TOE: Time of Emission • TOA: Time of Arrival. CSC774 - NCSU ADVANCED NETWORK SECURITY

10. Most important attacks on CRNs • Spectrum data falsification attacks: In this case, one or more SUs are compromised and hence report wrong sensing values to FC. This makes the FC make incorrect decision about the presence of PU. • The most preferred way to mitigate the attack is to collect sensing values from a group of SUs and remove the outlier values. CSC774 ADVANCED NETWORK SECURITY

11. Primary User Emulation Attack: Primary Transmitter PU1 PU2 SU2 SU1 PU3 CSC774 - NCSU ADVANCED NETWORK SECURITY

12. Primary User Emulation Attack: Primary Transmitter PU1 PU2 Attacker SU2 SUs cannot access channel as they think PU is transmitting SU1 PU3 CSC774 - NCSU ADVANCED NETWORK SECURITY

13. Why are we facing this attack : • Secondary users cannot authenticate the PU transmission. • FCC states that PU cannot be modified to support security. Hence regular authentication schemes don’t work. CSC774 - NCSU ADVANCED NETWORK SECURITY

14. General approaches to defeat this attack: Solution 1 • RSSI based PU localization: Decision is made based on all received sensing reports (x,y) FC RSSI values are measured at all SUs and calculate the location of PU. Ideal case of a PU transmitting, all RSSI values will be correct w.r.t distance CSC774 - NCSU ADVANCED NETWORK SECURITY

15. Solution 1 proposed by: • Zhou Yuan et al, suggested the use of localization schemes to estimate and authenticate the location of PU. • Scheme based on Received signal power. • Pr = Pt + a 10 log (do/d) + w • It can be defeated by attacker by using Antenna arrays with different power levels. CSC774 - NCSU ADVANCED NETWORK SECURITY

16. General approaches to defeat this attack: Solution 2 • Dr. PengNing et al proposed integrating cryptographic signatures and wireless link signatures to enable primary user detection. Essential to the approach is a helper node placed physically close to a primary user. CSC774 - NCSU ADVANCED NETWORK SECURITY

17. General approaches to defeat this attack: Solution 2 • Working with helper nodes. Helper Node (x,y) Helper node transmits signals identical to PU SUs can try to verify the PU authenticity by verifying the Wireless Link signature of Helper node CSC774 - NCSU ADVANCED NETWORK SECURITY

18. General approaches to defeat this attack: Solution 2 • This technique is very effective in terms of authenticating primary user. We exploit the proximity of Helper node with PU. • Problem is the authentication of wireless link signature of the helper node. Also if attackers are placed near helper nodes, then it causes problems. CSC774 - NCSU ADVANCED NETWORK SECURITY

19. General approaches to defeat this attack: Solution 3 • IRIS model proposed by Alexander et al, has a secure attack detection by verifying the consistency of system state (Transmit power and path loss). • This technique is very effective and it defeats both Data Falsification attacks and PUEA. But, it fails in the case of attacker with antenna arrays and directional antenna. CSC774 - NCSU ADVANCED NETWORK SECURITY

20. Attack model: Assumptions : • All nodes are loosely time synchronized. • Location of PU is fixed and known to all SUs. • Fusion Center is used to make decision about presence of PU. • All SUs are connected to FC using a secure link. • There is a LOS path between every SU and PU. CSC774 - NCSU ADVANCED NETWORK SECURITY

21. Attack model : Motivation • This attack model fails all the localization based solutions for PUEA which have been proposed previously. • Attacker uses a multi antenna array or MIMO technology with directional antennas to send PU-TX like signals to different SUs with various power levels faking the presence of PU. CSC774 - NCSU ADVANCED NETWORK SECURITY

22. Attack model: Representation • The power levels at different nodes are expected with respect to the distance from the PU-TX. CSC774 - NCSU ADVANCED NETWORK SECURITY

23. Attack model: • Antenna array – multiple antenna transmitter CSC774 - NCSU ADVANCED NETWORK SECURITY

24. Attack model: • This attack is possible because: • 1. Antenna arrays are low cost and easy to setup • 2. Attacker can manipulate the power levels in each directional beam from every antenna element to make sure every SU calculates the RSSI equal to the RSSI when PU transmits. CSC774 - NCSU ADVANCED NETWORK SECURITY

25. Attack model: Validation • We have simulated the attack model to verify whether such an attack is really possible. • Modeler: Opnet Network modeler 16 CSC774 - NCSU ADVANCED NETWORK SECURITY

26. Attack model: Directional Antenna pattern formation in Opnet CSC774 - NCSU ADVANCED NETWORK SECURITY

27. Attack model: Directional Antenna pattern formation in Opnet CSC774 - NCSU ADVANCED NETWORK SECURITY

28. Attack model: Directional Antenna pattern formation in Opnet CSC774 - NCSU ADVANCED NETWORK SECURITY

29. Attack model: A sample scenario proving the possibility of attack CSC774 - NCSU ADVANCED NETWORK SECURITY

30. Attack model: Throughput graphs. PU-TX (antenna 1) SU-1 SU-2 CSC774 - NCSU ADVANCED NETWORK SECURITY

31. Attack model: Multiple antenna array simulation. Ref: http://fens.sabanciuniv.edu/telecom/eng/comnet/cisco/smart.htm CSC774 - NCSU ADVANCED NETWORK SECURITY

32. Attack model: Validation • Hence if the attacker can configure each antenna element with the appropriate power levels to produce required RSSI values at each SU, then attack is achieved. • Regular localization based methods cannot defeat this attack. This forms the motivation for our solution. CSC774 - NCSU ADVANCED NETWORK SECURITY CSC774 DVANCED NETWORK SECURITY

33. Time of Emission Estimation Based Approach : Our solution to PUEA CSC774 - NCSU ADVANCED NETWORK SECURITY

34. Model PU SU SU PUE Fusion Center SU SU CSC774 - NCSU ADVANCED NETWORK SECURITY

35. Assumptions • Secondary Users and Fusion Center • are loosely Synchronized • have secure communication • Fusion Center • cannot be compromised • knows locations of all users (secondary as well as primary) • has good computational power and storage CSC774 - NCSU ADVANCED NETWORK SECURITY

36. Attacker Capabilities • Can use antenna array • But transmitting with a beam formation at different locations at different times is restricted. • Multiple Attackers can coordinate • They can be synchronized among themselves • Attacker knows location of all nodes • SU may be compromised CSC774 - NCSU ADVANCED NETWORK SECURITY

37. Proposed Approach • Sensors measure Time of Arrival • Fusion Center estimates Time of Emission • Robust against, • Multiple, coordinated attackers • Multiple compromised secondary users • Node with Antenna Array! CSC774 - NCSU ADVANCED NETWORK SECURITY

38. Design Estimate TOA! PU SU TOA Estimate TOA! TOA SU PUEA result PUEA result TOE estimated for every sensor must be almost same in an ideal scenario Fusion Center Estimate TOE! In the presence of an attack there will be deviations in some TOE estimations CSC774 - NCSU ADVANCED NETWORK SECURITY

39. Intuition Time CSC774 - NCSU ADVANCED NETWORK SECURITY

40. Procedure FC TOA TOA TOA TOA TOA FOR EACH NODE MEASURE TOE! TOEi = TOAi – Dist/c + ξ COMPUTE MEAN TOEmean CSC774 - NCSU ADVANCED NETWORK SECURITY

41. Procedure FOR EACH NODE, MEASURE DEVIATION! δi = TOEAVG ~ TOEi If δi > μ Increment C μ -> Maximum allowable deviation! C -> number of deviated values If C > k then PUEA! k -> Maximum no. of allowable deviated reports CSC774 - NCSU ADVANCED NETWORK SECURITY

42. Parameters! • Determining μ • The maximum deviation in the measurement by a node under a non-attack scenario! • Determining k • Too small? Increase in false negative! • Too large? Increase in false alarm! • Tradeoff needed! CSC774 - NCSU ADVANCED NETWORK SECURITY

43. Simulation Results CSC774 - NCSU ADVANCED NETWORK SECURITY

44. Limitation • If an attacker is capable of compromising almost every node! • Attacker too powerful! • Note: We have a threshold which is used to tolerate certain number of configured node compromises. But, if almost all nodes in network are compromised, then the network is not useful. CSC774 - NCSU ADVANCED NETWORK SECURITY

45. Future work • FCC may relax rule “no modification to the incumbent (primary) system should be required to accommodate opportunistic use of the spectrum by secondary users” • Already relaxed for wireless microphones • Removing Fusion Center • May decrease latency and increase performance of system. CSC774 - NCSU ADVANCED NETWORK SECURITY

46. Summary • An Attack Model against the approaches using RSSI is proposed and simulated • A Novel approach to mitigate PUEA is proposed using Time of Emission Estimation and simulated • Approach is compared with a similar RSSI based approach CSC774 - NCSU ADVANCED NETWORK SECURITY

47. Thank you! CSC774 - NCSU ADVANCED NETWORK SECURITY