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Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
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改进AppAuthentix推系统,使用先进的机器学习技术来识别真实和假冒的Android应用程序.

Ramnath M1, Yesubai Rubavathi C2

  • 1Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India.

PeerJ. Computer science
|December 9, 2024
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概括

本研究介绍了一种使用卷积神经网络 (CNN) 和自然语言处理 (NLP) 的新型应用程序识别方法,以提高应用商店的安全性. 该方法在检测欺诈性应用程序方面达到98.25%的准确性,提高了用户的信心.

关键词:
应用程序识别 应用程序识别在AppAuthentix推器中使用 AppAuthentix.卷积神经网络 (CNN) 是一种神经网络.伪造的应用程序是假的移动应用程序 移动应用程序自然语言处理 (NLP) 是一种自然语言处理.

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科学领域:

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

背景情况:

  • 智能手机应用生态系统的快速扩张导致了假冒和恶意应用程序的增加.
  • 现有的安全措施不足以有效地区分合法和有害的应用程序,给消费者和应用程序供应商带来风险.
  • 迫切需要先进的技术解决方案来增强应用商店的安全性和用户信任.

研究的目的:

  • 开发和评估一种用于验证移动应用程序和保护应用商店的新系统.
  • 为应对数字市场中越来越多的欺诈和有害应用程序的威胁.
  • 提高客户对移动应用平台的信心.

主要方法:

  • 利用卷积神经网络 (CNN) 来分析应用数据的图像.
  • 使用自然语言处理 (NLP) 来从应用程序相关文本中提取特征.
  • 集成了一个新的算法,AppAuthentix Recommender,用于强大的应用程序识别和身份验证.

主要成果:

  • 综合系统在识别合法和假冒移动应用程序方面表现出高准确度.
  • 在估计移动应用程序真实性方面取得了令人印象深刻的98.25%的准确率.
  • 开发的技术显著提高了应用商店的安全性,并使有效的移动应用程序验证成为可能.

结论:

  • 该研究提出了一种突破性的移动应用程序识别方法,在快速应用程序开发的时代至关重要.
  • CNN,NLP和AppAuthentix推算法的结合大大提高了应用商店的安全性.
  • 这些进步有助于更安全的移动应用程序使用和增加消费者对数字市场的信任.