一个智能垃圾邮件检测框架,利用垃圾邮件行为和语言的融合
Amna Iqbal1, Muhammad Younas2, Muhammad Kashif Hanif1
1Department of Computer Science, Government College University Faisalabad, Faisalabad, Punjab, Pakistan.
PloS one
|February 6, 2025
概括
这项研究引入了一个新的垃圾邮件检测框架,SD-FSL-CLSTM,有效地整合了语言和行为特征. 该模型通过自动学习功能交互来实现高精度,改善垃圾邮件审查分类.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 垃圾邮件的检测受到各种垃圾邮件的策略和现有功能工程方法的局限性的挑战.
- 手动的功能选择可能会导致过度装配和计算效率低下,而忽视重要的功能会影响性能.
- 当前的深度学习模型往往无法捕捉复杂的特征依赖性,无法将语言与行为方面整合起来.
研究的目的:
- 为有效地检测垃圾邮件,应对功能选择和不断演变的垃圾邮件行为方面的挑战.
- 识别用于垃圾邮件检测的最有效的特征和模式子集.
- 开发一个综合语言和行为特征的综合模型,以提高垃圾邮件检测的准确性.
主要方法:
- 提出了一个新的垃圾邮件检测框架,SD-FSL-CLSTM,利用垃圾邮件行为和语言特征的融合.
- 采用深度学习方法 (CLSTM) 来自动学习功能交互和依赖关系.
- 集成多种功能以捕捉影响垃圾邮件审查特征的复杂关系.
主要成果:
- SD-FSL-CLSTM框架证明了语言和行为特征的有效融合.
- 该模型在训练过程中自动学习了集成特征之间的复杂相互作用.
- 在检测和分类垃圾邮件评论方面实现了最低97%的准确性.
结论:
- 拟议的SD-FSL-CLSTM框架通过整合多种功能,为垃圾邮件检测提供了一个有希望的解决方案.
- 功能互动的自动学习显著提高了垃圾邮件审查分类的准确性.
- 这种方法有效地解决了以前方法在捕捉特征依赖性和演变的垃圾邮件行为方面的局限性.
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