A Policy Driven AI-Assisted PoW Framework
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.
Code (1)
Similar Papers 제목 키워드 기반
LLM-Assisted Behavioural and Scenario Augmentation for Agent-Based Energy Adoption Models
Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting structured behavioural assumptions and exploratory techno-economic scenar…
AI-Assisted Adaptive Rendering for High-Frequency Security Telemetry in Web Interfaces
Modern cybersecurity platforms must process and display high-frequency telemetry such as network logs, endpoint events, alerts, and policy changes in real time. Traditional rendering techniques based on static pagination…
SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints
The paradigm of robot-assisted surgery is shifting toward data-driven autonomy, where policies learned via Reinforcement Learning (RL) or Imitation Learning (IL) enable the execution of complex tasks. However, these ``bl…
Reinforcement LearningWireless Power Transfer and Intent-Driven Network Optimization in AAVs-assisted IoT for 6G Sustainable Connectivity
Autonomous Aerial Vehicle (AAV)-assisted Internet of Things (IoT) represents a collaborative architecture in which AAV allocate resources over 6G links to jointly enhance user-intent interpretation and overall network pe…
Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies
Deep reinforcement learning has achieved impressive success in control tasks. However, its policies, represented as opaque neural networks, are often difficult for humans to understand, verify, and debug, which undermine…
Reinforcement Learning