A Secure Communication Protocol For Wireless Biosensor Networks

1. A Secure Communication Protocol For Wireless Biosensor Networks Masters Thesis by Krishna Kumar Venkatasubramanian Committee: Dr. Sandeep Gupta Dr. Rida Bazzi Dr. Hessam Sarjoughian

2. Overview • Introduction • Problem Statement • System Model • Proposed Protocols • Security Analysis • Implementation • Conclusions & Future Work

3. Biomedical Smart Sensors • Miniature wireless systems. • Worn or implanted in the body. • Prominent uses: • Health monitoring. • Prosthetics. • Drug delivery. • Each sensor node has: • Small size. • Limited • memory • processing • communication capabilities sensors Base Station Communication links Environment (Human Body)

4. Motivation for biosensor security • Collect sensitive medical data. • Legal requirement (HIPAA). • Attacks by malicious entity: • Generate fake emergency warnings. • Prevent legitimate warnings from being reported. • Battery power depletion. • Excessive heating in the tissue.

5. Problem Statement • Direct communication to the BS can be prohibitive. • To minimize communication costs, biosensors can be organized into specific topologies. • Cluster topology is one of the energy-efficient communication topologies for sensor networks [HCB00]. • Traditional cluster formation protocol is not secure. • We want to develop protocols which allow for secure cluster formation in biosensor networks.

6. Cluster Topology Base Station Cluster Member Cluster Cluster head

7. Traditional Cluster Formation Protocol CH3 CH1 CH2 2 5 3 1 4 Weaker signal Environment

8. Security Flaws • HELLO Flood and Sinkhole Attack • The sinkhole can now mount selective forwarding attacks on the biosensors in its “cluster”. • Malicious entity can mount a Sybil attack where it presents different identities to remain CH in multiple rounds. Malicious Entity acting as a SINKHOLE CH2 CH1 2 3 1 Weaker signal

9. Node with surrounding tissue at above normal temperature. Node with surrounding tissue at normal temperature. tissue Security Flaws contd.. • Malicious entity sending bogus messages to sensor and depleting its energy. Node with dead battery Network Partitioning. • Malicious entityhaving unnecessary communication with a sensor causing heating in the nearby tissue.

10. System Model Glucose sensor Temperature sensor • ADVERSARIES: • Passive: Eavesdrop on communication and tamper with it. • Active: Physically compromise the external biosensors.

11. Trust Assumptions • The wireless communication is broadcast in nature and not trusted. • The biosensors do not trust each other. • Base Station is assumed not to be compromised.

12. Key Pre-Deployment • Each biosensor shares a unique pair-wise key (master key) with the BS. This key is called NSK • We do not use NSK directly for communication, we derive 4 keys from it (derived keys):

13. Biometrics • Physiological parameters like heart rate and body glucose. • Used for securing/authenticating communication between two biosensors which do not share any secret. • Usage Assumptions: • Only biosensors in and on the body can measure biometrics. • There is a specific pre-defined biometric that all biosensors can measure.

14. Issues with Biometrics • Biometric value data-space is not large enough. • Possible Solutions: • Combine multiple biometric values. • Take multiple biometric measurements at each time. • Limit the validity time of a biometric value. • Biometric values at different sites produce different values. • Solution Proposed in Literature: • These differences are independent. [Dau92] • Can be modeled as channel errors. [Dau92] • Fuzzy commitment scheme based on [JW99] used to correct differences. • Can correct up to two bit errors in the biometric value measured at the sender and receiver.

15. Time-Period 1 2 3 4 5 6 BMT ST Biometric Authentication Biometric Measurement Schedule Measure biometric: BioKey Measure biometric: BioKey’ Generate data Receive Msg: data, Cert [data] SENDER RECEIVER Compute Certificate: Cert [data] = MAC ( KRand, data), γ γ = KRand  BioKey Compute MAC Key: KRand’ = γ BioKey’ f (KRand’) = KRand Send Msg: data, Cert [data] Compute Certificate MAC And compare with received: MAC (KRand, data)

16. Centralized Protocol Execution Base Station CH 2 CH 3 CH 3 CH1 CH 1 CH 2 CH 3 Sensor Node Nodej All:IDj, NonceNj, MAC(K’Nj – BS, IDj | NonceNj), Cert[IDj, NonceNj] CHp BS: IDj, NonceNi , MAC(K’Nj – BS, IDj | NonceNi), CHp, SS, E<K CHp-BS, Cntr>(KCH-N), MAC(K’CHp – BS, CHp | SS | E<K CHp-BS, Cntr>(KCH-N) | Cntr) BS  Nodej :CHp, E<K BS-Nj, Cntr’> (KCH-N), Cntr’, MAC(K’BS-Nj, CHp | NonceNj | Cntr’ | E<K BS-Nj, Cntr’> (KCH-N))

17. Distributed Protocol Execution CH 3 CH 1 CH 2 Sensor Node CHj All:CHj, NonceCHj, E<KRand, Cntr>(Ktemp), Cert[IDj, Cntr, NonceCHj], λ λ = BioKey  KRand Nodek CHz: IDk, MAC (Ktemp, IDk | NonceCHz | Cntr | CHz)

18. Extensions • Distribute keys based on attributes. • Allows efficient data communication. • The BS distributes the keys. • For centralized ABK, sent during cluster formation. • For distributed separate step needed.

19. Security Analysis (Passive Adversary) • Hello Flood and Sinkhole Attack Centralized: • Malicious entity does not have appropriate keys to pose as legitimate CH. • Distributed: • Malicious entity cannot compute biometric certificate.

20. Security Analysis (Passive Adversary) • Sybil Attack • No entity can become part of network without having appropriate keys. • Identity Spoofing • Cannot pose as BS, no pair-wise (derived) keys. • Cannot pose as CH, no keys to authenticate data to BS. • Cannot pose as sensor node, cannot measure biometric to fool CH.

21. Security Analysis (Active Adversary) • CH compromise • Centralized: Security policy at BS to limit number of sensor nodes in a cluster. • Distributed: Need intruder monitoring scheme. • Sensor Node compromise • Intruder monitoring scheme needed for both protocols.

22. Implementation • We have implemented the two cluster formation protocols and their extensions. • The implementation was done on the Mica2 sensor motes. • We used TinyOS sensor operating system for writing our programs. • For security primitives TinySec used.

23. Implementation contd.. • Encryption – SkipJack • Message Authentication Code – CBC-MAC • We had 4 sensor nodes 3 CH and 1 BS in our implementation. • We simulated two main attacks on our implementation, both of which failed: • HELLO Flood attack. • Identity spoofing of sensor node to infiltrate the network.

24. Comparison • Security adds a overhead to the protocol. • We compared overhead in terms of energy consumption. • To compare the protocols, we analyzed them using the communication model given in [HCB00]. • Etrans = Etx * k + Ecx * k * d2 • Erecp = Erx * k

25. Security Overhead Comparison of Secure (without extension) and Non-secure Cluster Formation Protocols (CH = 5%)

26. Extension Overhead Comparison for Secure Cluster Formation Protocols with their extensions (CH = 5%)

27. Conclusions & Future Work • Protocols developed successfully prevent many of the potent attacks on the traditional cluster formation protocol. • Biometric based authentication used for ensuring authentication without previous key exchange. • Biometrics not traditionally random and schemes are needed to randomize them. • Better error correction schemes are needed which can correct larger differences in measured biometrics.

28. Reference [JW99] Ari Juels and Martin Wattenberg. “A fuzzy commitment scheme”. 1999. [Dau92] J. Daugman, “High Confidence personal identification by rapid video analysis of iris texture”, IEEE International Carnahan Conference on Security Technology, pp 50-60, 1992. [LGW01] L. Schwiebert, S. K. S. Gupta, J. Weinmann et al., “Research Challenges in Wireless Networks of Biomedical Sensors”, The Seventh Annual International Conference on Mobile Computing and Networking, pp 151-165, Rome Italy, July 2001. [HCB00] W. Rabiner Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks”, Proceedings of the 33rd International Conference on System Sciences (HICSS '00), January 2000.