SoftwareApplication
name: Code and dataset supporting the publication "STARVESPAM: Mitigating Spam with Local Reputation in Permissionless Blockchains"
description: Code and dataset supporting the publication "STARVESPAM: Mitigating Spam with Local Reputation in Permissionless Blockchains"
description: This deposit contains the full research artifact accompanying the paper "STARVESPAM: Mitigating Spam with Local Reputation in Permissionless Blockchains" (BRAINS 2025). The artifact includes (i) the datasets used in our experiments, (ii) the Python implementation of STARVESPAM and baseline policies, and (iii) scripts to reproduce the quantitative results and paper figures. Methodology and techniques: Problem setting: permissionless blockchain transaction propagation (gossip) under adversarial transaction spam.Approach: local reputation-based spam mitigation. Each peer maintains per-sender state over a sliding window of recent activity and derives a reputation value that influences filtering and (probabilistic) acceptance of incoming transactions. The design is intentionally local (no global coordination or trusted identities) and targets scalable spam suppression.Baselines implemented: Naive propagation (no filtering), Fee Filter (threshold-based), BanMan (strike/ban logic), EIP-1559-inspired behavior, SIMD-110-inspired behavior, and STARVESPAM (local reputation + throttling/filtering).Experiments: (1) Transaction-level filtering outcomes per policy (spam accepted / honest dropped) using a labeled dataset; (2) Propagation experiment measuring how widely transactions spread through a randomized overlay when filtering is enabled. Outputs are written as CSV logs and rendered as publication-ready PDF graphs.Statistics/metrics: per-policy outcome rates derived from logged decisions; confidence intervals are computed where applicable (see analysis scripts). Data and provenance: Raw input data (Otherside NFT mint transactions) is included under data/raw/otherside_nft_mint.csv.A processed/labeled dataset used by experiments is included under data/processed/labeled_ethereum_dataset.csv.The labeling/preprocessing pipeline is provided in data/scripts/labelling.py. This script documents the heuristics/features used to label transactions and produces the processed dataset used b…
code repository: https://data.4tu.nl/v3/datasets/7aae9528-e48b-44af-bccb-02d74b9f38ea.git
author: https://orcid.org/0000-0001-9322-4131
author: https://orcid.org/0000-0002-7205-2913
author: Person
  given name: Jeremie
  family name: Decouchant
  name: Jeremie Decouchant
author: Person
  given name: Johan
  family name: Pouwelse
  name: Johan Pouwelse
keywords: Decentralized reputation
keywords: Sybil attack
keywords: Blockchain
keywords: Spam mitigation
keywords: Peer-to-Peer
programming language: Markdown
programming language: Other
programming language: Python
programming language: TeX
programming language: large-text-file
license:
  creative work: creativeWork
    name: MIT
reference publication: brains67003.2025.11302925

https://orcid.org/0000-0001-9322-4131
type: Person
given name: Rowdy
family name: Chotkan
name: Rowdy Chotkan

https://orcid.org/0000-0002-7205-2913
type: Person
given name: Bulat
family name: Nasrulin
name: Bulat Nasrulin

brains67003.2025.11302925
type: ScholarlyArticle
name: STARVESPAM: Mitigating Spam with Local Reputation in Permissionless Blockchains
pagination: 1-9
url: https://doi.org/10.1109%2Fbrains67003.2025.11302925