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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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使用基于流量分析的监督机器学习技术检测Android勒索软件

Amnah Albin Ahmed1, Afrah Shaahid1, Fatima Alnasser1

  • 1Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

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概括
此摘要是机器生成的。

这项研究探讨了用于检测安卓勒索软件的人工智能 (AI). 开发了机器学习和深度学习模型,决策树 (DT) 显示高精度和支持矢量机 (SVM) 实现完美的回忆.

关键词:
安卓安全安卓安全安卓安全网络攻击,网络攻击.深度学习是一种深度学习.组合学习组合学习机器学习是机器学习.勒索软件的攻击是勒索软件的攻击.

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科学领域:

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

背景情况:

  • 安卓设备越来越多地成为网络犯罪分子对数据盗窃和勒索软件攻击的目标.
  • 勒索软件构成重大威胁,导致数据丢失,财务损失和运营中断.
  • 现有的基于人工智能的安卓勒索软件检测方法有希望,但需要进一步探索,特别是合奏和深度学习模型.

研究的目的:

  • 为安卓勒索软件检测开发和评估高效,精确和强大的机器学习 (ML) 和深度学习 (DL) 模型.
  • 为了比较各种ML和DL算法的性能,包括集体和表格注意力网络.
  • 调查特征选择对模型性能对良性和勒索软件流量的二进制分类的影响.

主要方法:

  • 利用了一个公开可用的数据集,其中包含392,035条良性和10种类型的Android勒索软件流量的记录.
  • 经过训练和测试的决策树 (DT),支持向量机器 (SVM),K-最近邻居 (KNN),集合模型,前神经网络 (FNN) 和表格注意力网络 (TabNet).
  • 进行了两个实验:一个使用所有数据集特征,另一个使用前19个特征.

主要成果:

  • 决策树 (DT) 获得了最高的准确性 (97.24%),精度 (98.50%),和F1得分 (98.45%).
  • 支持矢量机 (SVM) 显示完美召回 (100%),表明有效识别了所有勒索软件实例.
  • 这两项实验都取得了出色的结果,突出了部署的ML和DL技术的有效性.

结论:

  • 在构建强大的Android勒索软件检测系统时,ML和DL技术是有效的.
  • 该研究为模型性能提供了有价值的见解,DT和SVM显示了特殊的优势.
  • 进一步的研究可以建立在这些发现的基础上,以加强对不断变化的网络威胁的移动安全.