Abstract
A sharding mechanism could
potentially be the solution to enhance the scalability of blockchain
networks and makes the distributed ledger technology more feasible.
Despite the scalability improvement, it increases the influence of
malicious attacks on blockchain networks. We develop a comprehensive
trust model by enhancing the trust score of nodes to minimize the
adversary influences of malicious attacks in sharding based blockchain
networks. Firstly, a penalty factor is incorporated into this trust
model to decrease the probability of malicious nodes becoming leaders in
the shards. Then, we examine the leader selection probability for
varying penalty factors. We also observe the influence of the global
reputation on the trust score for a varying number of nodes. Secondly,
we increase the trustworthiness of nodes by including penalty factors
and reputation scores to nodes that could then identify the malicious
influence. The fair node distribution among shards is achieved by
distributing the nodes with the same aggregated trustworthiness scores.
Finally, we develop a probability distribution model to identify the
probabilities of clustering corrupted nodes into single shards and the
existence of such corrupted shards in the entire network. Uncorrupted or
honest shard probability is shown to be higher in the RapidChain than
the Elastico and OmniLedger sharding protocols. This could be as a
result of the shard resiliency of the RapidChain (υ/2) protocol being
more significant than that of the Elastico (υ/3) and in OmniLedger (υ/3)
protocols. Low message complexity of single intra-shard consensus of
the RapidChain protocol O(υ) may contribute to perform security
algorithms more efficiently than that of the Elastico O(υ2) and
OmniLedger O(υ) sharding protocols. The probabilities of clustering
corrupted nodes into single shards can be estimated, and the existence
of such corrupted shards in entire networks can be identified using the
proposed model.
| Original language | English |
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| Title of host publication | 2020 IEEE 3rd International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) |
| Place of Publication | United States |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 162-167 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728187082 |
| DOIs | |
| Publication status | E-pub ahead of print - Dec 2020 |
| Event | 3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020 - Virtual conference, Irvine, United States Duration: 09 Dec 2020 → 11 Dec 2020 https://ieeexplore.ieee.org/xpl/conhome/9355437/proceeding (conference proceedings) https://semanticcomputing.wixsite.com/aike2020 (conference website) |
Publication series
| Name | Proceedings - 2020 IEEE 3rd International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020 |
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Conference
| Conference | 3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2020 |
|---|---|
| Country/Territory | United States |
| City | Irvine |
| Period | 09/12/20 → 11/12/20 |
| Other | Artificial Intelligence (AI) is concerned with computing technologies that allow machines to see, hear, talk, think, learn, and solve problems. The huge potential of applying AI for problem solving represents an exciting future that business objectives can be achieved much more effectively. In addition, business-business, business-customer, and customer-customer may be interconnected in a revolutionary way to stimulate tremendous amount of interesting activities. A core of AI is Knowledge Engineering that addresses the acquistion, understanding, description, storage, integration, processing, control, and use of knowledge, among others. The Third IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE 2020), is an international forum for academia and industries to exchange visions and ideas in the state of the art and practice of Artificial Intelligence and Knowledge Engineering, as well as to identify the emerging topics and define the future of Artificial Intelligence and Knowledge Engineering. |
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