论文标题
政策驱动AI辅助POW框架
A Policy Driven AI-Assisted PoW Framework
论文作者
论文摘要
基于工作证明（POW）的网络防御系统需要传入的网络请求，以消耗努力解决任意数学难题。当前的艺术状态无法区分可信赖和不信任的连接，这需要所有人解决复杂的难题。在本文中，我们介绍了一个由人工智能（AI）辅助的POW框架，该框架利用基于IP流量的功能来告知自适应发行者，然后可以产生具有不同硬度的难题。模块化框架使用这些功能来确保不信任的客户端解决难题，从而产生比实际请求更长的延迟，以接收服务器的响应。我们的初步发现揭示了我们的方法有效地防护了不可信的交通。
Proof of Work (PoW) based cyberdefense systems require incoming network requests to expend effort solving an arbitrary mathematical puzzle. Current state of the art is unable to differentiate between trustworthy and untrustworthy connections, requiring all to solve complex puzzles. In this paper, we introduce an Artificial Intelligence (AI)-assisted PoW framework that utilizes IP traffic based features to inform an adaptive issuer which can then generate puzzles with varying hardness. The modular framework uses these capabilities to ensure that untrustworthy clients solve harder puzzles thereby incurring longer latency than authentic requests to receive a response from the server. Our preliminary findings reveal our approach effectively throttles untrustworthy traffic.
