Byzantine agreement (BA) is a foundational building block in distributed systems that has been extensively studied for decades. With the growing demand for protocol composition in practice, the security analysis of BA protocols under multi-instance executions has attracted increasing attention. However, most existing adversary models focus solely on party corruption and neglect important threats posed by adversarial manipulations of communication channels in the network. Through channel attacks, messages can be reordered across multiple executions and lead to violations of the protocol’s security guarantees, without the participating parties being corrupted. In this work, we present the first adversary model that combines party corruption and channel attacks. Based on this model, we establish new security thresholds for Byzantine agreement under parallel and concurrent compositions, supported by complementary impossibility and possibility results that match each other to form a tight bound. For the impossibility result, we show that even authenticated Byzantine agreement protocols cannot be secure under parallel composition when n ≤ 3t or n ≤ 2c + 2t + 1, where t and c denote the number of corrupted parties and communication channels, respectively. For the possibility result, we prove the existence of secure protocols for unauthenticated Byzantine agreement under parallel and concurrent composition, when n > 3t and n > 2c+2t+1. More specifically, we provide a general black-box compiler that transforms any single-instance secure BA protocol into one that is secure under parallel executions, and we provide a non-black-box construction for concurrent compositions.

Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments. In this survey, we examine RL’s role in strategic mining analysis, comparing it to MDP-based approaches. We begin by reviewing foundational MDP models and their limitations, before exploring RL frameworks that can learn near-optimal strategies across various protocols. Building on this analysis, we compare RL techniques and their effectiveness in deriving security thresholds, such as the minimum attacker power required for profitable attacks. Expanding the discussion further, we classify consensus protocols and propose open challenges, such as multi-agent dynamics and real-world validation. This survey highlights the potential of reinforcement learning to address the challenges of selfish mining, including protocol design, threat detection, and security analysis, while offering a strategic roadmap for researchers in decentralized systems and AI-driven analytics.

Log-based anomaly detection plays a crucial role in ensuring the reliability of systems. While deep learning-based small detection models (SDMs) are efficient, the large language models (LLMs) are accurate and capable of providing explanations. Intuitively, a compelling question arises: Can we seamlessly combine the advantages of both approaches? In this work, we delve into this underexplored research direction and propose CoLA, a novel collaborative log anomaly detection framework. During collaborative inference, an SDM serves as a filter to select potentially anomalous instances, while a downstream LLM acts as an expert to detect anomalies, offer explanations, and refine the SDM. Extensive experiments on three large real-world datasets demonstrate that CoLA significantly outperforms state-of-the-art methods in terms of effectiveness, efficiency, and explainability, while also greatly reducing labor costs.

Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight.

We consider a hierarchical structure of a permissioned blockchain with three types of participant: providers, collectors, and governors. Providers forward transactions to collectors; collectors upload received transactions to governors after verifying and labeling them; and governors validate a portion of the labeled transactions they receive, pack valid transactions into a block, and append the block to the ledger. This model has various fields of application including data collection from the Internet-of-Things and second-hand markets. Our main contribution is to propose a reputation-based protocol to help governors evaluate the reliability of collectors. Specifically, given a transaction, each governor runs a softmax-based function to calculate a probability for each collector that sent and labeled this transaction. The probabilities, calculated using collectors’ reputations as inputs, represent the likelihood of the lead governor selecting the labeled transaction from collectors to consider for further validation. After the lead governor verifies a transaction, all collectors’ reputations are updated in line with the agreement of their labeling and the validity of the transaction as found by the lead governor. We show, both theoretically and empirically, that our protocol can significantly reduce governors’ verification workloads while maintaining firm liveness and high incentives.

Nowadays, sharding is deemed a promising way to save traditional blockchain protocols from their low scalability. However, such a technique also brings several potential risks and a huge communication burden. An improper design may give rise to an inconsistent state among different committees. Further, the communication burden arising from cross-shard transactions, unfortunately, reduces the system's performance. In this paper, we first summarize five essential issues that all sharding blockchain designers face. For each issue, we discuss its key challenge and propose our suggested solutions. In order to break the performance bottlenecks, we design a committee structure and propose a reputation mechanism for selecting leaders. The term reputation in our design reflects each node's honest computation resources. In addition, we present a recovery procedure in case the leader is malicious. Theoretically, we prove that the system is robust under our design. Further simulation results also support this. In addition, the results show that selecting leaders by reputation can dramatically improve the system's performance.

Public goods projects, including open source technology, client development, and blockchain knowledge education, play an important role in the flourishing blockchain ecosystem. Accordingly, decision making for public goods funding is a key issue in the studies of the blockchain ecosystem. This work develops a human oracle protocol approach, involved with public goods projects, experts, and funders, as a solution to the public goods investment problem on blockchain. In our human oracle, funders contribute their investments, which are stored in a funding pool. Experts provide investment advice on public goods projects based on their experience. Decisions made by the human oracle on the amount of support from the funding pool are based on experts’ reputation. The reputation of each expert is updated by the performance of the project’s implementation in comparison to her advice. That is, better investment performance brings a higher reputation. Besides being applied to static model, our human oracle can also be extended to accommodate dynamic settings, in which the experts might leave or join the decision-making process. We introduce a regret bound to measure the effectiveness of our human oracle. Theoretically, we prove an upper regret bound for both static and dynamic models, and prove its tightness with an asymptotically equal lower bound. Empirically, we show that our oracle’s investment decision is close to the optimal investment in hindsight.

Permissioned blockchains take more reliability on participants than permissionless ones. In this poster, we focus on a hierarchical scenario of permissioned blockchains, which includes three types of participants: providers, collectors, and governors. Such a scenario has many applications in the field of IoT data collection, horizontal strategic alliances, etc. Our object is to reduce the cost of the governor's transaction verification. For this purpose, we propose a reputation protocol to help the governor measure the reliability of collectors. Based on the measurement of collectors' reputations, governors can pack high-quality transactions from reliable collectors into blocks, and thus the cost of verifying transactions can be decreased effectively. Through theoretical analysis, our protocol dramatically reduces the verification loss of governors.

Traditional public distributed ledgers have not been able to scale-out well and work efficiently. Sharding is deemed as a promising way to solve this problem. By partitioning all nodes into small committees and letting them work in parallel, we can significantly lower the amount of communication and computation, reduce the overhead on each node’s storage, as well as enhance the throughput of the distributed ledger. Existing sharding-based protocols still suffer from several serious drawbacks. The first thing is that all non-faulty nodes must connect well with each other, which demands a huge number of communication channels in the network. Moreover, previous protocols have faced great loss in efficiency in the case where the honesty of each committee’s leader is in question. At the same time, no explicit incentive is provided for nodes to actively participate in the protocol.We present CycLedger, a scalable and secure parallel protocol for distributed ledger via sharding. Our protocol selects a leader and a partial set for each committee, who are in charge of maintaining intra-shard consensus and communicating with other committees, to reduce the amortized complexity of communication, computation, and storage on all nodes. We introduce a novel semi-commitment scheme between committees and a recovery procedure to prevent the system from crashing even when leaders of committees are malicious. To add incentive for the network, we use the concept of reputation, which measures each node’s trusty computing power. As nodes with a higher reputation receive more rewards, there is an encouragement for nodes with strong computing ability to work honestly to gain reputation. In this way, we strike out a new path to establish scalability, security, and incentive for the sharding-based distributed ledger.