TY - GEN
T1 - MRL-PoS
T2 - 2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024
AU - Islam, Tariqul
AU - Bappy, Faisal Haque
AU - Shaila Zaman, Tarannum
AU - Islam Sajid, Md Sajidul
AU - Mehedi Ahsan Pritom, Mir
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The core of a blockchain network is its consensus algorithm. Starting with the Proof-of-Work, there have been various versions of consensus algorithms, such as Proof-of-Stake (PoS), Proof-of-Authority (PoA), and Practical Byzantine Fault Tolerance (PBFT). Each of these algorithms focuses on different aspects to ensure efficient and reliable processing of transactions. Blockchain operates in a decentralized manner where there is no central authority and the network is composed of diverse users. This openness creates the potential for malicious nodes to disrupt the network in various ways. Therefore, it is crucial to embed a mechanism within the blockchain network to constantly monitor, identify, and eliminate these malicious nodes. However, there is no one-size-fits-all mechanism to identify all malicious nodes. Hence, the dynamic adaptability of the blockchain network is important to maintain security and reliability at all times. This paper introduces MRL-PoS, a Proof-of-Stake consensus algorithm based on multi-agent reinforcement learning. MRL-PoS employs reinforcement learning for dynamically adjusting to the behavior of all users. It incorporates a system of rewards and penalties to eliminate malicious nodes and incentivize honest ones. Additionally, MRL-PoS has the capability to learn and respond to new malicious tactics by continually training its agents.
AB - The core of a blockchain network is its consensus algorithm. Starting with the Proof-of-Work, there have been various versions of consensus algorithms, such as Proof-of-Stake (PoS), Proof-of-Authority (PoA), and Practical Byzantine Fault Tolerance (PBFT). Each of these algorithms focuses on different aspects to ensure efficient and reliable processing of transactions. Blockchain operates in a decentralized manner where there is no central authority and the network is composed of diverse users. This openness creates the potential for malicious nodes to disrupt the network in various ways. Therefore, it is crucial to embed a mechanism within the blockchain network to constantly monitor, identify, and eliminate these malicious nodes. However, there is no one-size-fits-all mechanism to identify all malicious nodes. Hence, the dynamic adaptability of the blockchain network is important to maintain security and reliability at all times. This paper introduces MRL-PoS, a Proof-of-Stake consensus algorithm based on multi-agent reinforcement learning. MRL-PoS employs reinforcement learning for dynamically adjusting to the behavior of all users. It incorporates a system of rewards and penalties to eliminate malicious nodes and incentivize honest ones. Additionally, MRL-PoS has the capability to learn and respond to new malicious tactics by continually training its agents.
KW - Blockchain
KW - Distributed Consensus
KW - Multi-agent Systems
KW - Proof-of-Stake
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85186749210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85186749210&partnerID=8YFLogxK
U2 - 10.1109/CCWC60891.2024.10427777
DO - 10.1109/CCWC60891.2024.10427777
M3 - Conference contribution
AN - SCOPUS:85186749210
T3 - 2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024
SP - 409
EP - 413
BT - 2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024
A2 - Paul, Rajashree
A2 - Kundu, Arpita
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 January 2024 through 10 January 2024
ER -