A Policy Driven AI-Assisted PoW Framework
This addresses the issue of inefficient cyberdefense for network security systems by providing a more targeted approach, though it appears incremental as it builds on existing PoW methods with AI enhancements.
The paper tackles the problem of Proof of Work (PoW) cyberdefense systems being unable to differentiate between trustworthy and untrustworthy connections, resulting in an AI-assisted framework that uses IP traffic features to adaptively assign puzzle hardness, effectively throttling untrustworthy traffic.
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.