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Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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相关实验视频

Updated: Jul 4, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

异常检测IDS用于检测基于机器学习算法的物联网网络中的DoS攻击.

Esra Altulaihan¹, Mohammed Amin Almaiah^2,3,4, Ahmed Aljughaiman¹

¹Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Sensors (Basel, Switzerland)

|January 26, 2024

概括

本研究介绍了一种使用机器学习的入侵检测系统 (IDS),用于对抗物联网 (IoT) 网络中的拒绝服务 (DoS) 攻击. 与遗传算法特征选择的决策树和随机森林分类器显示了物联网安全的最佳性能.

关键词:

美国国防部的攻击是DoS攻击.在IDS IDS中,您可以使用物联网 (IoT) 网络物联网 (IoT) 的网络物联网.分类器算法分类器算法功能选择功能选择机器学习是机器学习.

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Last Updated: Jul 4, 2025

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科学领域:

网络安全网络安全
机器学习机器学习
物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

物联网 (IoT) 系统面临着越来越多的网络安全威胁,包括拒绝服务 (DoS) 攻击,因为它们的自我配置和开放性质.
这些攻击损害了用户的安全和隐私,导致货币损失和服务中断.
保护物联网网络免受复杂的网络攻击是个人和组织的重要和日益关注的问题.

研究的目的:

提出和评估一个入侵检测系统 (IDS),旨在增强物联网 (IoT) 网络的安全性,特别是针对拒绝服务 (DoS) 攻击.
利用异常检测和机器学习 (ML) 技术来识别和减轻物联网环境中的这些威胁.

主要方法:

开发了一个IDS,使用异常检测来监控物联网网络流量是否偏离正常行为.
使用了四种监督机器学习分类器:决策树 (DT),随机森林 (RF),K近邻 (kNN) 和支持矢量机器 (SVM).
使用基于关联的特征选择 (CFS) 算法和基因算法 (GA) 进行了特征选择,性能与IoTID20数据集进行了比较.

主要成果:

决策树 (DT) 和随机森林 (RF) 分类器在使用遗传算法 (GA) 选择它们的特征时获得了最佳性能.
虽然DT和RF表现出高准确度,但决策树 (DT) 分类器在训练和测试时间方面表现优越.
拟议的IDS有效地使用IoTID20数据集识别了物联网网络中的异常活动.

结论:

该研究成功地证明了基于ML的IDS在保护物联网网络免受DoS攻击方面的有效性.
将遗传算法特征选择与决策树或随机森林分类器相结合,为强大的物联网安全提供了一个有希望的方法.
决策树分类器为物联网系统中的实时DoS攻击检测提供了性能和效率的良好平衡.