The proliferation of spam on the Web has necessitated the development of machine learning models to automate their detection. However, the dynamic nature of spam and the sophisticated evasion techniques employed by spammers often lead to low accuracy in these models. Traditional machine-learning approaches struggle to keep pace with spammers' constantly evolving tactics, resulting in a persistent challenge to maintain high detection rates. To address this, we propose blockchain-enabled incentivized crowdsourcing as a novel solution to enhance spam detection systems. We create an incentive mechanism for data collection and labeling by leveraging blockchain's decentralized and transparent framework. Contributors are rewarded for accurate labels and penalized for inaccuracies, ensuring high-quality data. A smart contract governs the submission and evaluation process, with participants staking cryptocurrency as collateral to guarantee integrity. Simulations show that incentivized crowdsourcing improves data quality, leading to more effective machine-learning models for spam detection. This approach offers a scalable and adaptable solution to the challenges of traditional methods.

翻译：网络垃圾信息的泛滥使得开发机器学习模型以实现自动化检测成为必要。然而，垃圾信息的动态特性以及垃圾信息发送者采用的复杂规避技术，常导致这些模型的准确性较低。传统的机器学习方法难以跟上垃圾信息发送者不断演变的策略，导致维持高检测率成为一项持续挑战。为解决这一问题，我们提出基于区块链的激励众包作为一种新颖的解决方案，以增强垃圾信息检测系统。我们利用区块链去中心化和透明的框架，创建了一种用于数据收集和标注的激励机制。贡献者因提供准确标注而获得奖励，因标注不准确而受到惩罚，从而确保数据的高质量。智能合约管理提交和评估过程，参与者以加密货币作为抵押品进行质押以保证诚信。模拟实验表明，激励众包提高了数据质量，从而产生了更有效的垃圾信息检测机器学习模型。该方法为传统方法面临的挑战提供了一种可扩展且适应性强的解决方案。