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相关概念视频

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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一个新的优化神经网络模型用于网络攻击检测,使用增强的鱼优化算法.

Koganti Krishna Jyothi1, Subba Reddy Borra2, Koganti Srilakshmi3

  • 1Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, TS, 501301, India.

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概括

凭证填充攻击威胁到网络安全. 一个新的增强鱼优化算法-人工神经网络 (EWOA-ANN) 模型有效地检测和预测这些网络威胁,提高了帐户的保护.

关键词:
人工神经网络 (ANN) 是一个人工神经网络.凭证填充充满了他们的资格.网络攻击 网络攻击

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科学领域:

  • 网络安全和网络安全网络安全.
  • 人工智能在网络安全中的作用
  • 计算智能是一种计算智能.

背景情况:

  • 在我们互联的数字世界里,网络安全至关重要.
  • 凭据填充攻击,利用被盗凭据,对在线账户安全构成重大威胁.
  • 在多个平台上广泛重复使用密码加剧了这一漏洞.

研究的目的:

  • 为了应对检测和预测凭据填充攻击的挑战.
  • 引入一种用于增强网络安全防御的新型模型.
  • 提高在线账户的安全性,防止未经授权的访问.

主要方法:

  • 开发一种新的增强鱼优化算法 (EWOA),用于培训.
  • 将EWOA与人工神经网络 (ANN) 集成,以创建EWOA-ANN模型.
  • 使用EWOA-ANN模型进行凭证填充攻击的检测和预测.

主要成果:

  • 拟议的EWOA-ANN模型在识别凭据填充攻击方面表现出有效性.
  • 该模型在预测这些网络威胁的发生和性质方面表现有希望.
  • 计划进行经验性比较,以验证模型的性能与现有的安全分析相比.

结论:

  • EWOA-ANN模型为打击凭据填充网络攻击提供了一个强大的解决方案.
  • 这种方法增强了对在线安全至关重要的检测和预测能力.
  • 该研究贡献了一种新的优化技术,用于提高网络安全应用中的神经网络性能.