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TBDD: A New Trust-based, DRL-driven Framework for Blockchain Sharding in IoT. (arXiv:2401.00632v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
Integrating sharded blockchain with IoT presents a solution for trust issues
and optimized data flow. Sharding boosts blockchain scalability by dividing its
nodes into parallel shards, yet it's vulnerable to the $1\%$ attacks where
dishonest nodes target a shard to corrupt the entire blockchain. Balancing
security with scalability is pivotal for such systems. Deep Reinforcement
Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional
optimization. This paper introduces a Trust-based and DRL-driven
(\textsc{TbDd}) framework, crafted to counter shard collusion risks …
attacks balancing security blockchain corrupt data flow framework iot nodes scalability security sharding solution target trust trust issues vulnerable