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Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

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Updated: Jan 11, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

评估机器学习方法用于多次攻击分类,以提高物联网网络中的计算效率.

Maher Alharby^1,2

¹Department of Cybersecurity, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia. mharby@taibahu.edu.sa.

Scientific reports

|November 14, 2025

概括

此摘要是机器生成的。

这项研究通过使用机器学习来检测拒绝服务等网络攻击来增强物联网 (IoT) 的安全性. 开发的模型实现了近乎完美的准确性,并大大缩短了检测时间.

关键词:

在CICIoT2023数据集中,网络攻击就是网络攻击.美国国防部的攻击是DoS攻击.物联网安全物联网安全物联网安全机器学习机器学习

相关实验视频

Last Updated: Jan 11, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

科学领域:

网络安全网络安全
机器学习机器学习
网络安全网络安全

背景情况:

物联网 (IoT) 设备的扩散带来了重大的安全漏洞.
物联网网络越来越容易受到复杂的网络攻击,包括拒绝服务 (DoS) 和分布式拒绝服务 (DDoS).

研究的目的:

在物联网环境中开发和评估基于机器学习的方法来检测和分类DoS,DDoS和Mirai攻击.
评估用于入侵检测的各种监督学习算法的性能和计算效率.

主要方法:

使用CICIoT2023数据集与五个监督算法:随机森林,梯度提升,天真贝斯,决策树和K-最近邻居.
实施了数据预处理,包括对类不平衡进行低采样,并使用Chi-square,PCA和随机森林回归器进行特征选择.
基于准确度,精度,灵敏度,F1分数,训练时间和预测时间的评估模型.

主要成果:

在CICIoT2023数据集上实现了99.99%的最先进的准确性,超过了现有的研究.
决策树模型展示了卓越的计算效率,大幅减少了训练和预测时间,同时保持了高准确度.
该研究提供了针对物联网入侵检测的精选机器学习算法的全面性能和效率比较.

结论:

机器学习为在物联网网络中检测和分类网络攻击提供了高效和高效的解决方案.
开发的方法为为资源有限的物联网环境创建强大的,计算效率高的入侵检测系统提供了实际意义.
进一步的研究可以建立在这些发现的基础上,以提高快速扩展的物联网生态系统的安全态度.