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概括
此摘要是机器生成的。

这项研究通过开发一个模型来通过机器学习和深度学习来识别smishing消息来增强垃圾邮件检测. 该KNN-Flatten模型实现了94.13%的准确性,提供了改进的实时检测能力.

关键词:
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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 网络安全 网络安全

背景情况:

  • 不被请求的短信,称为smishing,以及数据的不规则性构成了重大安全挑战.
  • 现有的垃圾邮件检测方法往往忽视了文本,图像和文本在smishing攻击中的复杂相互作用.
  • 需要先进的模型,能够有效地分析这些多方面的关系.

研究的目的:

  • 开发和评估一个复杂的模型来检测Smishing消息.
  • 探索将传统机器学习和深度学习技术用于垃圾邮件检测的有效性.
  • 分析词语,图像和情境因素之间的关系,以识别smishing.

主要方法:

  • 将UCI垃圾邮件数据集与真实世界的垃圾邮件数据合并,使用光学字符识别 (OCR) 进行图像分析.
  • 使用传统的机器学习模型 (K-means,NMF,GMM) 与特征提取 (TF-IDF,PCA).
  • 使用深度学习模型 (RNN-Flatten,LSTM,Bi-LSTM) 来捕捉顺序依赖和上下文细微差别.

主要成果:

  • 通过向量化器进行K-means特征提取,获得了91.01%的准确性.
  • 该KNN-Flatten模型表现出卓越的性能,达到94.13%的准确性.
  • 对比分析强调了机器学习和深度学习方法在垃圾邮件检测中的优势.

结论:

  • 由于其高精度,KNN-Flatten模型显示了实时粉碎检测的巨大潜力.
  • 虽然有效,但KNN-Flatten等先进模型的计算复杂性可能会影响大规模部署.
  • 带有向量化器的K-means提供了良好的准确性,但可能需要持续重新训练以适应不断变化的smishing战术.