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D EFENSE A GAINST S PECTRUM S ENSING D ATA F ALSIFICATION A TTACKS I N C OGNITIVE R ADIO N ETWORKS Li Xiao Department of Computer Science & Engineering.

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Presentation on theme: "D EFENSE A GAINST S PECTRUM S ENSING D ATA F ALSIFICATION A TTACKS I N C OGNITIVE R ADIO N ETWORKS Li Xiao Department of Computer Science & Engineering."— Presentation transcript:

1 D EFENSE A GAINST S PECTRUM S ENSING D ATA F ALSIFICATION A TTACKS I N C OGNITIVE R ADIO N ETWORKS Li Xiao Department of Computer Science & Engineering Michigan State University - Chowdhury Sayeed Hyder, Brendan Grebur and Li Xiao

2 Outline Background ◦ Cognitive Radio Network ◦ The IEEE Standard SSDF Attacks Problem Statement ◦ Attack Model Existing solutions ARC scheme Simulation Results ◦ Error rate ◦ True/ false detection rate Securecomm

3 Background Figure: Current Spectrum Allocation in US Figure: Underutilized Spectrum Ref: Akyildiz, I., W. Lee, M. Vuran, and S. Mohanty, “NeXt Generation/ Dynamic Spectrum Access/ Cognitive Radio Wireless Networks: A Survey”, Computer Networks 2006 Securecomm

4 Background Current Status ◦ Spectrum Scarcity ◦ Underutilized spectrum Cognitive radio (CR) ◦ Adapt its transmission and reception parameters (frequency, modulation rate, power etc.) Securecomm

5 Background Cognitive Radio Network ◦ Two types of user  Primary user or licensed user (PU)  Secondary user or opportunistic user (SU) ◦ Requirements  SU cannot affect ongoing transmission of PUs  Must vacant the spectrum if PU arrives ◦ Spectrum Sensing Securecomm

6 Background IEEE standard ◦ Centralized, single hop, point to multipoint ◦ Collaborative spectrum sensing  Quiet Periods (QP)  Sensing period and frequency  Must vacant at the arrival of PU  False alarm and misdetection rate ◦ Inter cell synchronization Securecomm

7 Background SU BS PU Figure: CRN Architecture Vulnerable against security threats!! Securecomm 2011 Ref: K. Bian and J. Park, “Security vulnerabilities in IEEE ”, Proceedings of the 4th Annual International Conference on Wireless Internet WICON '08 7

8 SSDF Attacks SU BS PU Figure: CRN Architecture How can BS defend against the SSDF attack ? Securecomm Independent Attack - Collaborative Attack 8

9 Problem Statement Network Model ◦ Attack Model ◦ Independent Attack  Attack randomly ◦ Collaborative Attack  Going Against Majority Attack  Subgroup Attack Our goal is to minimize the error in deciding about the spectrum availability by BS in addition to detecting the attackers and reducing the false detection rate. Securecomm

10 Problem Statement Detection probability of an honest user (P d ) Detection probability of an independent attacker (P d m ) Detection probability of collaborative attackers (Q d m ) Securecomm

11 Problem Statement Attackers’ goal ◦ Increase the error rate and disguise their intention. ◦ Collaboration makes it easier. BS’s goal ◦ Correct decision making. ◦ Identify attackers and minimize the impact of their collaboration. Solution ◦ Reduce their strength of collaboration. ◦ Differentiate between honest and dishonest nodes. 11 Securecomm 2011

12 Existing Solutions Reputation based [1] ◦ BS fails to take a correct decision for 35% attackers ◦ High misdetection rate K-nearest neighbor [2] ◦ Works well for independent SSDF attack ◦ Threshold selection is critical Securecomm Ref: [1] A. Rawat, P. Anand, C. Hao and P. Varshney, “Collaborative Spectrum Sensing in the Presence of Byzantine Attacks in Cognitive Radio Networks”, IEEE Transactions on Signal Processing 2011 [2] H. Li and Z. Han, “Catching Attackers for Collaborative Spectrum Sensing in Cognitive Radio Systems: An Abnormality Detection Approach”, DySPAN 2010.

13 Adaptive Reputation-based Clustering (ARC) Scheme 13 Securecomm 2011 Collection: BS collects node reports Clustering: k-medoid clustering using PAM Voting: Intra-cluster weighted voting Inter cluster majority voting Feedback: Cluster adjustment Reputation adjustment

14 Adaptive Reputation-based Clustering (ARC) Scheme Intra-cluster weighted voting ◦ Further from median, less voting power Majority cluster voting ◦ Decision of majority clusters becomes the final decision Securecomm

15 Adaptive Reputation-based Clustering (ARC) Scheme Clusters with poor reputation removed Number of clusters is adjusted Reputation of nodes is adjusted based on ◦ cluster's vote ◦ distance from median and ◦ node’s current vote Securecomm

16 Results Simulation tool: MATLAB Simulation Parameters ◦ Number of attackers 10% - 50% ◦ Probability of attack 0.1 – 1.0 ◦ Number of runs – 10 ◦ Prob. of false alarm 0.1 ◦ Prob. of miss detection 0.1 Securecomm

17 Results Compared to Rawat et al. reputation-based method (R) Collaborative and Independent SSDF attacks ◦ Number of attackers ◦ Probability of attack ◦ Probability of detection Performance metrics ◦ Probability of error (Q E ) ◦ Attacker Detection Rate (Q D ) ◦ Attacker Misdetection Rate (Q F ) Rawat Reputation Method: 1)Node majority vote for N frames. 2)Remove users with M or more differences. 3)Repeat. Securecomm

18 Results Securecomm Figure 1: Q D, Q E, Q F vs # of attackers Collaborative SSDF Attack Significant improvement in reducing error rate Moderate true detection rate Huge improvement in reducing false detection rate

19 Results Securecomm Figure 2: Q D, Q E, Q F vs prob. of attack Collaborative SSDF Attack Huge improvement in reducing error rate Significant true detection rate Huge improvement in reducing false detection rate

20 Results Securecomm Figure 3: Q D, Q E, Q F vs prob. of detection Collaborative SSDF Attack Significant improvement in reducing error rate Moderate true detection rate Huge improvement in reducing false detection rate

21 Results 21 Securecomm 2011 Figure 4: Q D, Q E, Q F vs # of attackers Collaborative SSDF Attack (Subgroup attack) Huge improvement in reducing error rate Similar true detection rate Huge improvement in reducing false detection rate

22 Results 22 Securecomm 2011 Figure 5: Q D, Q E, Q F vs # of attackers Collaborative SSDF (GAMA) Attack Significant improvement in reducing error rate Moderate true detection rate Significant improvement in reducing false detection rate

23 Results Securecomm Figure 6: Q D, Q E, Q F vs # of attackers Independent SSDF Attack Similar error rate Moderate true detection rate Huge improvement in reducing false detection rate

24 Results Securecomm Figure 7: Q D, Q E, Q F vs prob. of attack Independent SSDF Attack Slight improvement in reducing error rate Similar true detection rate Significant improvement in reducing false detection rate

25 Results Securecomm Figure 8: Q D, Q E, Q F vs prob. of detection Independent SSDF Attack Slight improvement in reducing error rate Huge improvement in reducing false detection rate

26 Conclusion & Future Work Devised robust decision-making algorithm for CRNs Displays better performance than current schemes ARC can minimize error rate consistently ◦ Low attacker misdetection rates ◦ Does not require any prior knowledge ◦ Applicable to both Independent and Collaborative attacks Future Work ◦ Explore a GA method for determining optimal number of clusters (k values) ◦ Explore different attacking strategies Securecomm

27 Questions ?


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