网络安全教育与培训的强化学习范式

Professor Gabriel Kabanda, Colletor Tendeukai Chipfumbu, Tinashe Chingoriwo
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摘要

强化学习(RL)是一种机器学习,它涉及从与环境的相互作用中学习,以实现与环境条件相关的某些长期目标。强化学习发生在行动序列、观察和奖励被用作输入时,它是基于假设和目标导向的。异步强化学习算法主要有异步一步Q学习算法、异步一步SARSA算法、异步n步Q学习算法和异步优势Actor-Critic算法(A3C)。本文确定了网络安全教育与培训的强化学习(RL)范式。该研究主要采用实证主义研究哲学,侧重于确定网络安全教育和培训的RL范式的定量方法。研究设计是一个实验,重点是使用Python实现RL Q-Learning和A3C算法。异步优势Actor-Critic (A3C)算法在深度强化学习任务上更快、更简单、得分更高。该研究具有描述性、探索性和解释性。以津巴布韦商业银行为例,对网络安全教育培训进行了调查。研究对象包括津巴布韦五家商业银行的员工和客户,样本量为370人。深度强化学习(DRL)已被用于解决物联网中的各种问题。DRL大量利用A3C算法和一些Q-Learning,这可以用来对抗入侵主机或网络以及物联网设备中的虚假数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Reinforcement Learning Paradigm for Cybersecurity Education and Training
Reinforcement learning (RL) is a type of ML, which involves learning from interactions with the environment to accomplish certain long-term objectives connected to the environmental condition. RL takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The key asynchronous RL algorithms are Asynchronous one-step Q learning, Asynchronous one-step SARSA, Asynchronous n-step Q-learning and Asynchronous Advantage Actor-Critic (A3C). The paper ascertains the Reinforcement Learning (RL) paradigm for cybersecurity education and training. The research was conducted using a largely positivism research philosophy, which focuses on quantitative approaches of determining the RL paradigm for cybersecurity education and training. The research design was an experiment that focused on implementing the RL Q-Learning and A3C algorithms using Python. The Asynchronous Advantage Actor-Critic (A3C) Algorithm is much faster, simpler, and scores higher on Deep Reinforcement Learning task. The research was descriptive, exploratory and explanatory in nature. A survey was conducted on the cybersecurity education and training as exemplified by Zimbabwean commercial banks. The study population encompassed employees and customers from five commercial banks in Zimbabwe, where the sample size was 370. Deep reinforcement learning (DRL) has been used to address a variety of issues in the Internet of Things. DRL heavily utilizes A3C algorithm with some Q-Learning, and this can be used to fight against intrusions into host computers or networks and fake data in IoT devices.
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