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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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ONE3A:使用GAN网络和优化技术的智能手机的一对一身份验证模型.

Mohamed Meselhy Eltoukhy1, Tarek Gaber2,3, Abdulwahab Ali Almazroi1

  • 1Department of Information Technology, College of Computing and Information Technology at Khulais, University of Jeddah, Jeddah, Saudi Arabia.

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概括

本研究介绍了智能手机的高效隐式身份验证模型,使用合成数据生成和鱼优化算法 (WOA) 进行功能选择. WOA-RF模型显著提高了准确性,同时降低了移动安全计算成本.

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功能选择 功能选择生成性对抗性网络 (GAN) 是一种对抗性网络.随机的森林随机的森林智能手机身份验证认证鱼优化算法 (WOA) 是一种

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 智能手机面临着影响认证效率的计算限制.
  • 现有的身份验证方法可能需要额外的硬件或产生高计算成本.
  • 小数据集对开发强大的身份验证模型构成挑战.

研究的目的:

  • 为智能手机提出一个高效的隐式身份验证模型,克服计算限制.
  • 解决智能手机身份验证中的一对一分类问题.
  • 通过使用有限的资源来提高认证准确性.

主要方法:

  • 整合条件表式生成对抗网络 (CTGAN) 进行合成数据生成,以处理不平衡的数据集.
  • 开发一种基于鱼优化算法 (WOA) 的新型特征选择技术.
  • 使用RHU触摸移动按键数据集进行评估,将WOA与哈里斯·霍克斯优化 (HHO) 和随机森林 (RF) 分类器进行比较.

主要成果:

  • 在基于触摸行为的移动用户身份验证中,WOA-RF模型实现了最高的分类准确性 (99.62%±0.40%).
  • 与HHO算法相比,WOA显示出更高的减少率 (87.85%).
  • 拟议的模型有效地解决了一个对所有分类挑战,提高了效率.

结论:

  • 集成CTGAN和WOA-RF的新型隐式身份验证模型对于智能手机用户身份验证非常有效.
  • 这种方法克服了数据集限制和计算限制,为移动安全提供了实用的解决方案.
  • 在资源有限的设备上,WOA-RF模型在提高身份验证准确性和效率方面取得了重大进展.