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相关概念视频

Reinforcement01:23

Reinforcement

177
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
177

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相关实验视频

Updated: Jun 3, 2025

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
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通过强化学习和推系统进行自主透测试的分析.

Ariadna Claudia Moreno1, Aldo Hernandez-Suarez1, Gabriel Sanchez-Perez1

  • 1Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico.

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概括

本研究介绍了网络安全透测试 (pentesting) 的高级建议系统. 它使用机器学习和强化学习来提高漏洞检测的准确性和优化攻击策略.

关键词:
透测试是一种透测试.推者系统是推者系统.强化学习是一种强化学习.

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 透测试 (pentesting) 对于识别IT漏洞至关重要,但面临诸如分析工具虚假阳性等挑战.
  • 由于复杂和不可预测的环境,现有的压力测试方法需要大量的分析师专业知识.
  • 机器学习 (ML) 在异常检测方面表现有前途,但需要集成到动态测试工作流中.

研究的目的:

  • 开发一个智能系统,提高透测试的有效性和准确性.
  • 为了解决当前测工具和方法的局限性.
  • 为自动化脆弱性评估和利用策略选择提出一种新的方法.

主要方法:

  • 一个具有丰富的上下文,词汇意识的变压器模型处理了关于目标环境的问题.
  • 强化学习 (RL) 估计器评估并选择最佳的 pentest 策略.
  • 该系统基于学习的数据和环境背景,动态探索攻击向量.

主要成果:

  • 拟议的系统实现了超过97.0%的F1得分.
  • 确切匹配率超过97.0%,这表明准确度很高.
  • 在选择相关和最佳的测试策略方面表现出有效性.

结论:

  • 开发的系统显著提高了透测试的准确性和效率.
  • ML和RL的整合为复杂的网络安全挑战提供了强大的解决方案.
  • 这种方法提高了漏洞的识别,并加强了IT系统的预防性控制.