Collusion Attack from Hubs in the Blockchain Offline Channel Network
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The document discusses a research study on collusion attacks within the Bitcoin Lightning Network, focusing on how clusters of hubs can work together to launch such attacks. It presents a mathematical model using Banzhaf indices to evaluate the likelihood of collusion and identifies 62 nodes that are capable of executing attacks against 90% of their neighbors. The study includes an evaluation of the network's structure and the potential vulnerabilities related to collusion within the hub network.
Collusion Attack from Hubs in the Blockchain Offline Channel Network
- 1. Subhasis Thakur and John Breslin Data Science Institute National University of Ireland Galway Collusion attack from hubs in the blockchain offline channel network
- 2. “Collusion practices in a West of Ireland livestock mart” - Curtin & Varley, 1982 2
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- 21. 21 Research problem: collusion (eclipse) attack ● Can a collusion of hubs execute a collusion attack against another set of hubs?
- 22. Results ● Mathematical model of collusion attack on the channel network ● Likelihood of collusion attack using Banzhaf indices ● We found that there are 62 nodes who can execute collusion attacks against 90% of their neighbours in the Lightning Network 22
- 23. 23 Model of collusion attack Neighborhood of Vi with distance d
- 24. 24 Model of collusion attack Targets of a collusion attack is a pair of hubs in its neighborhood
- 25. 25 Model of collusion attack Vi forms a collusion to produce a cut between hubs V1 and V2 in its neighborhood
- 26. 26 Model of collusion attack: how likely that a hub can orch- estrate an attack? Collusion formation as a cooperative game
- 27. 27 Ability to orchestrate collusion = Banzhaf index
- 28. 28 Banzhaf index Banzhaf index = (Number of coalitions where Vi is a critical player) / (Number of coalitions where Vi is a member)
- 29. 29 Banzhaf index (in terms of the ability to orchestrate collusion) Banzhaf index = (Number of collusions where Vi is a critical player) / (Number of collusions where Vi is a member)
- 30. 30 Potential of collusion attacks A high standard deviation would indicate that there are few hubs who can easily execute a collusion attack while the remaining hubs are unlikely to execute collusion attack
- 31. 31 ● The probability that a hub can successfully execute a collusion attack increases as its degree increases ● If hubs of the hub network have a uniform degree then, Banzhaf indices are approximately equal ● Uniform Banzhaf indices may prevent collusion attacks in the hub network Method to reduce possibility of collusion attacks
- 32. 32 Evaluation: data Bitcoin Lightning Network (1st March 2019) ● # nodes = 2810 ● # edges = 22596 ● Average degree = 16 ● Minimum degree = 1 ● Maximum degree = 961
- 34. ● Relation between the Banzhaf index and the diameter of the network ● The uniformity of Banzhaf increases as the diameter of the network decreases 34
- 35. 35 Vulnerability due to collusion attack Vulnerability w.r.t. node Vi = (# of neighbours with a Banzhaf index less than Vi ) / (Size of neighbourhood of Vi )
- 36. 36 ● There are 62 nodes who can execute collusion attacks against 90% of their neighbors in the Lightning Network