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ISAnWin:使用深度CNN用于跨Windows和Android平台的恶意软件检测的感应式通用零射击学习.

Umm-E-Hani Tayyab1, Faiza Babar Khan1, Asifullah Khan2

  • 1Department of Computer & Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.

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概括
此摘要是机器生成的。

这项研究在罗神经网络中引入了一种新的ConvNet-6架构,用于有效地进行恶意软件检测的零射击学习. 该模型准确地识别出新的恶意软件变体,使用最小的数据,达到82%的准确性.

关键词:
算法和对算法的分析.安卓恶意软件安卓恶意软件人工智能的人工智能是人工智能.数据挖掘和机器学习数据科学是数据科学.深度学习是一种深度学习.终端保护的目的是保护终端.恶意软件检测检测 恶意软件检测恶意软件PE恶意软件PE恶意软件零射击学习的学习.

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科学领域:

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

背景情况:

  • 有效的恶意软件检测对于对抗不断发展的网络威胁的数字安全至关重要.
  • 数据稀缺,特别是用于跨家族恶意软件检测,是一个重大障碍.
  • 零射击学习提供了一种有希望的方法来克服恶意软件分类中的数据限制.

研究的目的:

  • 为罗神经网络提出一个新的ConvNet-6架构,以实现恶意软件检测的零射击学习.
  • 为了应对有限的标记训练数据在识别各种恶意软件家族的挑战.
  • 评估模型的性能和对未见的恶意软件变体的概括性.

主要方法:

  • 开发了一个集成到罗神经网络的ConvNet-6架构.
  • 采用零射击学习策略,对模型进行训练,每个子家族只有一种标记样本.
  • 在各种数据集上进行了实验,包括安卓和便携式可执行恶意软件家族.

主要成果:

  • 在测试数据集上获得了82%的准确性,证明了对新型恶意软件变体的有效检测.
  • 通过在Android恶意软件培训后在便携式可执行数据集上进行测试来展示模型的可转移性,性能一致.
  • 验证了在语网络中深层卷积神经网络 (CNN) 的潜力,用于跨家族恶意软件检测.

结论:

  • 在语网络中提出的ConvNet-6架构为跨家族恶意软件检测提供了有效的零射击学习.
  • 该模型表现出强大的概括能力和一致的性能,即使使用最小的标记训练数据.
  • 这种方法具有显著的潜力,可以增强针对复杂和新兴恶意软件威胁的网络安全防御.