一种策略驱动的人工智能辅助PoW框架
A Policy Driven AI-Assisted PoW Framework
摘要：
基于工作量证明（PoW）的网络防御系统需要传入的网络请求来解决任意数学难题。目前的技术水平无法区分可信和不可信的连接，需要所有人解决复杂的难题。在本文中，我们介绍了一种人工智能（AI）辅助的PoW框架，该框架利用基于IP流量的功能来通知自适应发行者，然后可以生成具有不同硬度的谜题。模块化框架使用这些功能来确保不可信的客户端解决更难的难题，从而比真实的请求从服务器接收响应的延迟更长。我们的初步研究结果表明，我们的方法有效地抑制了不可信的流量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
