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Masking and Demasking Agents

Masking and Demasking Agents

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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相关实验视频

Updated: Sep 14, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

增强的SqueezeNet模型用于检测物联网机器人攻击:一个全面的方法.

Balaganesh Bojarajulu¹, Sarvesh Tanwar¹, Thipendra Pal Singh²

¹Amity Institute of Information Technology, Amity University, Noida, India.

|July 24, 2025

概括

本研究介绍了一种改进的SqueezeNet-DCNN模型,用于增强物联网 (IoT) 安全性. 新框架有效地以高准确性和效率检测尸网络攻击,优于现有方法.

科学领域:

网络安全网络安全
机器学习机器学习
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

物联网 (IoT) 设备的快速扩张扩大了网络威胁,特别是针对网络安全的尸网络攻击.
目前用于检测这些威胁的机器学习 (ML) 和深度学习 (DL) 方法面临着准确性和计算需求的挑战,限制了它们在资源有限的物联网设置中的实时应用.

研究的目的:

开发一个先进的检测框架,提高在物联网环境中识别网络威胁的准确性和计算效率.
在物联网系统的约束范围内,解决当前ML / DL实时入侵检测方法的局限性.

主要方法:

改进的SqueezeNet模型与深度卷积神经网络 (DCNN) 和优化的随机混合LP层集成.
数据预处理涉及最小至最大的规范化,以实现一致的缩放和改进模型学习.
使用集成的SqueezeNet-DCNN架构进行了特征提取和分类.

主要成果:

拟议的模型实现了0.97.97的高分类准确度.
观察到明显减少的假阳性率为0.054.
实验结果表明,与Bi-GRU,CNN,PolyNet和LinkNet等既定技术相比,性能优越.

关键词:

尸网络攻击的攻击深度学习是一种深度学习.改进的挤压净值这就是为什么物联网物联网物联网.最小至最大的正常化.

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Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

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Published on: August 27, 2021

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

相关实验视频

Last Updated: Sep 14, 2025

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

结论:

增强的SqueezeNet-DCNN框架为物联网环境中的实时尸网络攻击检测提供了一个计算效率高,准确的解决方案.
拟议的模型在保护物联网网络免受复杂的网络威胁方面取得了重大进展.
这些发现表明该模型适合在资源有限的物联网安全应用中实际部署.