1. Soummya Kar NAS, Data Science Symposium Jun. 14, 2018
Secure Computing and Decision Making in the Internet of Things: Challenges and Approaches
Soummya Kar
NAS, Data Science Symposium
Jun. 14, 2018

2. Computing and Inference in IoT
Data from distributed sources
Make inferences (computations) based on data
(Envisioned) Distributed Fog Architecture

3. Data Integrity How trustworthy are sensors and data?
Malicious adversary hijacks devices, manipulates data
Security countermeasures?

4. Preventive Security “Traditional” Cyber-Security
Protect agents/data by preventing intrusion
Encryption: authentication, authorization

5. Preventive Security: Drawbacks
Lack of mitigation plans
Incomplete protection
High computational burden
Not well suited for fully distributed architectures

6. System Architectures Central Processor Fully Distributed
Single entity to protect
Access to all data
High computational resources
Many distributed entities to protect
Access to local data only
Agents have limited computational power

7. Security for Distributed Fog
(Distributed) Fog Architecture: Devices perform computations, P2P communications
Device computational limits
Preventive security not always feasible!

8. Reactive Security Prevention Data Goals Reaction
Responds to breakdowns in preventive security
E.g., intrusion detection/identification, resilient processing
Prevention
Data
Goals
Reaction

9. Many applications, problem variants! Classical to current themes
Distributed multi-processor networks: consensus, leader selection, synchronized execution…
Decision-making on distributed data: learning, inference, optimization.
Financial systems: distributed ledgers, blockchain…

10. Networked Parameter Estimation

11. Networked Parameter Estimation

12. Centralized Approach: Challenges

13. Centralized Approach
Properties (assuming observability of the collective sensing model):

14. Distributed Recursive Approach
No (possibly high-dimensional) raw data is exchanged among agents.

15. Distributed Recursive Approach

16. Consensus/Gossip Type Algorithms

17. Distributed Recursive Approach
Properties (assuming collective observability and network connectivity):

18. Distributed Estimation: Adversarial Scenariosθ*
Byzantine Agent Attack Model [Lamport, Shostak, Pease, ‘82]
Adversary hijacks and completely takes over specific agents within the network
Agents may send arbitrary messages, update estimates in any manner

19. Resilient distributed algorithm: basic idea

20. Major Challenges

21. t

22. Performance [Chen, Kar, Moura, 2017]
No Byzantines: All agents recover θ consistently, arbitrarily small false alarm probability
With Byzantines: Uncompromised agents will either recover θ consistently or determine that they are under attack
(Under assumption of observability by the network induced by uncompromised agents)

23. Connections with classical approaches

24. Some Thoughts

25. References
Y. Chen, S. Kar, and J. M. F. Moura, "The Internet of Things: Secure Distributed Inference," in IEEE Signal Processing Magazine, To appear, 2018.
Y. Chen, S. Kar and J. M. F. Moura, "Resilient Distributed Estimation Through Adversary Detection," in IEEE Transactions on Signal Processing, vol. 66, no. 9, pp , May, 2018.
Chen, Y., Kar, S., & Moura, J. M. (2017). Distributed Estimation Under Sensor Attacks. arXiv preprint arXiv:
S. Weerakkody, B. Sinopoli, S. Kar, and A. Datta, "Information flow for security in control systems," in IEEE Conference on Decision and Control, Dec , 2016, Las Vegas, NV.
A. Datta, S. Kar, B. Sinopoli and S. Weerakkody, "Accountability in cyber-physical systems," in Science of Security for Cyber-Physical Systems Workshop (SOSCYPS), Vienna, 2016, pp. 1-3.
Y. Chen, S. Kar and J. M. F. Moura, "Cyber-Physical Attacks with Control Objectives," in IEEE Transactions on Automatic Control, Vol. PP., pp. 1-8, Aug., 2017.
Y. Chen, S. Kar and J. M. F. Moura, "Dynamic Attack Detection in Cyber-Physical Systems with Side Initial State Information," in IEEE Transactions on Automatic Control, Vol. 62, No. 9, pp , Sep., 2017.