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Masking and Demasking Agents01:19

Masking and Demasking Agents

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
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多模式双嵌入网络用于恶意软件开放集识别.

Jingcai Guo, Han Wang, Yuanyuan Xu

    IEEE transactions on neural networks and learning systems
    |March 20, 2024
    PubMed
    概括

    本研究介绍了MDENet用于恶意软件开放式识别 (MOSR),通过使用多式联络功能和双嵌入来改善已知和未知的恶意软件家族的检测. 该方法增强了特征多样性,以获得更好的分类和检测性能.

    科学领域:

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

    背景情况:

    • 恶意软件开放式识别 (MOSR) 对于识别已知和新型恶意软件家族至关重要.
    • 现有的MOSR方法由于已知和未知的恶意软件之间的相似特征分布而陷入困境.
    • 在MOSR中基于值的检测可能导致未知样本被错误地归类为已知的样本.

    研究的目的:

    • 提出一种新的方法,MDENet,用于增强恶意软件开放式识别.
    • 通过利用多式联络信息来提高恶意软件特征的辨别能力.
    • 解决现有的MOSR技术在处理特征分布相似性的局限性.

    主要方法:

    • 开发了使用数字和文本恶意软件特征的多式联运双嵌入网络 (MDENet).
    • 利用多尺度CNN对恶意软件图像进行数字特征编码.
    • 使用语言模型来编码文本特征,创建代表向量.
    • 实施在歧视性和排他性空间中的双重嵌入,具有对比的学习和球体规范化.
    • 将MAL-100数据集增强为MAL-100+数据集,具有多式联运特征.

    主要成果:

    • MDENet有效地增强了功能多样性,导致了更具代表性和歧视性的恶意软件表示.

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  • 双嵌入策略改善了已知家族的分类和未知家族的检测.
  • 邮件和MAL-100+数据集的实验结果验证了拟议方法的有效性.
  • 丰富的MAL-100+数据集为未来的MOSR研究提供了宝贵的资源.

    • 结论:

    • 通过有效地融合多式联网功能,MDENet在恶意软件开放式识别方面取得了重大进展.
    • 拟议的双嵌入方法提高了恶意软件分类和检测的稳定性和准确性.
    • 这项工作为MOSR领域提供了新的架构和改进的数据集.