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SYSTEMS AND METHODS FOR LEARNING-BASED HIGH-PERFORMANCE, ENERGY-EFFICIENT, AND SECURE ON-CHIP COMMUNICATION DESIGN FRAMEWORK

机译:基于学习的高性能,节能和安全的片上通信设计框架的系统和方法

摘要

Systems and methods are disclosed for improving on-chip security, while minimizes the latency and cost of security techniques to improve system-level performance and power simultaneously. The framework uses machine learning algorithms, such as an artificial neural network (ANN), for runtime attack detection with higher accuracy. Further, a learning-based attack mitigation method using deep reinforcement learning is disclosed, where the method may be used to isolate the malicious components and to optimize network latency and energy-efficiency.
机译:公开了用于改善片上安全性的系统和方法,同时最大限度地减少安全技术的延迟和成本,以同时提高系统级性能和功率。 该框架使用机器学习算法,例如人工神经网络(ANN),用于具有更高的准确度的运行时攻击检测。 此外,公开了一种使用深增强学习的基于学习的攻击缓解方法,其中该方法可用于隔离恶意组件并优化网络延迟和节能。

著录项

  • 公开/公告号US2021342690A1

    专利类型

  • 公开/公告日2021-11-04

    原文格式PDF

  • 申请/专利权人 THE GEORGE WASHINGTON UNIVERSITY;

    申请/专利号US202117307563

  • 发明设计人 KE WANG;HAO ZHENG;AHMED LOURI;

    申请日2021-05-04

  • 分类号G06N3/08;G06N3/04;G06K9/62;H04L29/06;

  • 国家 US

  • 入库时间 2022-08-24 22:04:47

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