Mitigation of Spectrum Sensing Data Falsification in Cognitive Radio Networks

Mitigation of the Spectrum Sensing Data Falsifying Attack in Cognitive Radio Networks Rajorshi Biswas, Jie Wu, and Xiaojiang Du Dept. of Computer and Information Sciences Temple University

Road Map Cooperative spectrum sensing 1. Previous work 2. Majority voting and existing models 3. Weighted majority voting with confidence 4. Simulation results 5. Extension: two-level majority voting 6. Conclusion 7.

1. Cooperative Spectrum Sensing Primary user (PU) Licensed user, high priority 1 1 1 Secondary user (SU) Send sensing information to aggregator Aggregator 1 PU 1 SU PU=1 Malicious secondary user (MSU) Send opposite sensing information to aggregator 0 Aggregator Does majority voting Results are sent back to SUs and MSUs 0 1 1 PU 0 SU s wrong sensing and MSU s result can change aggregator s decision PU=1 1 0 MSU Solution: reduce weight of MSUs through reputation

2. Previous Work MSU detection systems Limitations Linear reputation-based system. SUs send raw sensing information to Aggregator. If a SU agrees with aggregator, reputation increases by 1 or decreases by 1 . Computation overhead is high for processing raw sensing information. Linear reputation update mechanisms are not efficient enough. R. Chen, J. -. Park and K. Bian, "Robust Distributed Spectrum Sensing in Cognitive Radio Networks," IEEE INFOCOM 2008. Active transmission-based system. PU emulated signals can be detected. MSU will respond correctly to emulated signal. PU emulated signal Sensing result T. Bansal, B. Chen, and P. Sinha, Fastprobe: Malicious user detection in cognitive radio networks through active transmissions, in IEEE INFOCOM 2014.

3. Majority Voting and Existing Models ?? ?= ?? ?= normalized reputation If ?? ? > 0.5 Aggregator s decision=1 Else Aggregator s decision=0 Initial reputation=1 Update reputation of SUs and MSUs using Reputation Update Function (RUF) Aggregator makes decision

Majority Voting and Existing Models Linear RUF: ????= ? ????+ 1 ? ? ? = priority of previous reputation If SU s result = aggregator s result ? = 1 Else ? = 1 (or 0, no decrease) Multiplicative RUF: * ????= ????exp ?? ? = learning rate: [0, 1] Multiplicative RUF with sliding window: ????= ???? exp(????????? ??????) -1 1 1 1 -1 1 1 ????? Sliding window ???????? *Arora, Sanjeev, Elad Hazan, and Satyen Kale. "The multiplicative weights update method: a meta-algorithm and applications." Theory of Computing 8.1 (2012)

4. Weighted Majority Voting with Confidence ? = 0.01 Adaptive Multiplicative RUF SU1 SU2 SU3 MSU1 MSU2 1 1 1 1 1 2.71 2.71 2.71 2.71 0.36 7.38 20.08 54.59 1 2.71 7.38 20.08 54.55 7.38 2.71 0.13 0.36 ?? = Normalized confidence High variance reputation 7.38 ????= ???? exp(? ? ??) 1 confidence 0.13 Minority: 30 Majority: 70 1 0 1 A 1 0 ??=|70 30| 70 + 30= 0.4 Without confidence Time PU 0 1 100 0 200 0 300 1 400 1 Ground truth ? 0.6 0.2 0.6 0.2 0.2 1 1 1 1 1 1.82 2.22 4.05 1.82 1.49 2.71 1.82 2.22 4.05 1.82 2.22 1.22 0.54 0.44 0.81 SU1 SU2 SU3 MSU1 MSU2 4.95 3.32 4.95 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0 0 SU1 SU2 SU3 MSU1 MSU2 1 0 1 1 0 reputation Low variance Sensing result 1 0.67 Majority 1 0 1 1 voting A 1 A 1 1 0 1 0 With confidence False positive False negative

5. Simulation Settings and datasets Dataset type 1 Number of SUs: 10-50 SU MSU SU error rate: 0-0.3 (randomly selected) time Number of MSUs: 7-50 Dataset type 2 SU MSU error rate: 0.7-1 (randomly selected) MSU MSU time Initial reputation: 1 MSUs build reputation Attack Sensing records of 100,000 timeslots.

Multiplicative vs. Adaptive Multiplicative RUF SU/MSU: 50/50 ? = 0.2 SU/MSU: 50/50 ? = 0.1 SU/MSU: 50/40 ? = 0.1 SU/MSU: 50/40 ? = 0.2 Dataset type 1 Used for simulation. False positive (FP), False negative (FN), Error (ER), Adaptive multiplicative (ADA MULT), Multiplicative (MULT)

Multiplicative vs. Adaptive Multiplicative RUF Adaptive multiplicative Multiplicative Dataset type 2 with ? = 0.01 High variation of reputation in multiplicative RUF leads to more errors.

6. Extension: Two-Level Majority Voting 13 MSUs (clustered) 13 SUs 2 wrong aggregators 4 correct aggregators. PU SU Aggregator MSU MSUs may win in one-level voting MSU cannot win in two-level voting Connected dominating set based distributed aggregator selection process

Two-Level vs. One-Level Voting MSUs are sparse (uniform). MSUs are clustered. False positive (FP), False negative (FN), Error (ER), Level 1 (LVL 1), and Level 2 (LVL 2) 80% MSU: clustered 20% MSU and all SU: uniformly distributed Dataset type 2 with ? = 0.01 SU/MSU: 50/40 Two-level voting works better when MSUs are clustered

7. Summary Adaptive multiplicative RUF using confidence Adaptive multiplicative RUF works better than other RUFs Two-level voting produces less errors than one-level voting 1 1 1 Aggregat or 1 PU 1 S U PU=1 0