Mitigating Conflicting Transactions in Blockchain (ConChain)

IEEE GLOBECOM 2024 | IEEE BLOCKCHAIN 2024 | IEEE CCNC 2024

While blockchains initially gained popularity in the realm of cryptocurrencies, their widespread adoption is expanding beyond conventional applications, driven by the imperative need for enhanced data security. Despite providing a secure network, blockchains come with certain tradeoffs, including high latency, lower throughput, and an increased number of transaction failures. A pivotal issue contributing to these challenges is the improper management of “conflicting transactions,” commonly referred to as “contention”. When a number of pending transactions within a blockchain collide with each other, this results in a state of contention. This situation worsens network latency, leads to the wastage of system resources, and ultimately contributes to reduced throughput and higher transaction failures.



In response to this issue, in this work, we present a novel blockchain scheme that integrates transaction parallelism and an intelligent dependency manager aiming to reduce the occurrence of conflicting transactions within blockchain networks. In terms of effectiveness and efficiency, experimental results show that our scheme not only mitigates the challenges posed by conflicting transactions, but also outperforms both existing parallel and non-parallel Hyperledger Fabric blockchain networks achieving higher transaction success rate, throughput, and latency. The integration of our scheme with Hyperledger Fabric appears to be a promising solution for improving the overall performance and stability of blockchain networks in real-world applications.

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Multi-agent Reinforcement Learning based Proof of Stake Consensus (MRL-PoS)

IEEE GLOBECOM 2024 | IEEE CCWC 2024

Proof of Stake (PoS) blockchains offer promising alternatives to traditional Proof of Work (PoW) systems, providing scalability and energy efficiency. However, blockchains operate in a decentralized manner 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 without involving any central authority.



In this work, we propose MRLPoS+, a novel consensus algorithm to enhance the security of PoS blockchains by leveraging Multi-agent Reinforcement Learning (MRL) techniques. Our proposed consensus algorithm introduces a penalty-reward scheme for detecting and eliminating malicious nodes. This approach involves the detection of behaviors that can lead to potential attacks in a blockchain network and hence penalizes the malicious nodes, restricting them from performing certain actions. Our developed Proof of Concept demonstrates effectiveness in eliminating malicious nodes for six types of major attacks. Experimental results demonstrate that MRL-PoS+ significantly improves the attack resilience of PoS blockchains compared to the traditional schemes without incurring additional computation overhead.

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Secure and Efficient Dynamic Cloud Auditing

IEEE ICC 2024 | IEEE GLOBECOM 2023

articleRead the full paper on B-Tree  articleRead the full paper on Enhanced MHT