StarveSpam: Mitigating Spam with Local Reputation in Permissionless Blockchains

2025-09-27

AI Summary

Blockchain spam transactions cause mempool congestion, exorbitant fees, and degraded service quality; existing economic deterrents (e.g., gas pricing, staking) fail to distinguish malicious spam from low-value but legitimate activity. This paper proposes a decentralized local reputation defense protocol operating at the transaction forwarding layer, enabling fine-grained behavioral monitoring and distributed peer-to-peer node scoring, coupled with adaptive rate limiting—without requiring global consensus or on-chain protocol modifications. Its key innovation lies in a lightweight, locally observed reputation model that dynamically adjusts per-node forwarding thresholds, achieving precise suppression of large-scale spam while preserving inclusivity for honest low-value transactions. Evaluated on real-world Ethereum NFT spam event data, the scheme achieves >95% spam interception, incurs only a 3% loss rate for legitimate transactions, and reduces network exposure risk by 85% compared to rule-based baselines.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Responsible Web: Measurement, analysis, and circumvention of Web censorship
• Web Mining and Content Analysis: Content-based information diffusion
• Security and Privacy: Blockchains and distributed ledgers

Abstract

Spam poses a growing threat to blockchain networks. Adversaries can easily create multiple accounts to flood transaction pools, inflating fees and degrading service quality. Existing defenses against spam, such as fee markets and staking requirements, primarily rely on economic deterrence, which fails to distinguish between malicious and legitimate users and often exclude low-value but honest activity. To address these shortcomings, we present StarveSpam, a decentralized reputation-based protocol that mitigates spam by operating at the transaction relay layer. StarveSpam combines local behavior tracking, peer scoring, and adaptive rate-limiting to suppress abusive actors, without requiring global consensus, protocol changes, or trusted infrastructure. We evaluate StarveSpam using real Ethereum data from a major NFT spam event and show that it outperforms existing fee-based and rule-based defenses, allowing each node to block over 95% of spam while dropping just 3% of honest traffic, and reducing the fraction of the network exposed to spam by 85% compared to existing rule-based methods. StarveSpam offers a scalable and deployable alternative to traditional spam defenses, paving the way toward more resilient and equitable blockchain infrastructure.

Problem

• Mitigating blockchain spam through local reputation tracking
• Distinguishing malicious users from legitimate low-value activity
• Reducing spam exposure without requiring global consensus changes

Innovation

• Decentralized reputation protocol for spam mitigation
• Local behavior tracking with peer scoring system
• Adaptive rate-limiting without global consensus requirement