Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Force Classification01:22

Force Classification

2.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.3K
Aggregates Classification01:29

Aggregates Classification

970
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...
970
Classification of Systems-I01:26

Classification of Systems-I

552
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:
552
Classification of Systems-II01:31

Classification of Systems-II

458
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,
458

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Concept-Based Word Learning in Human Infants.

Psychological science·2015
Same author

Vitexin reduces hypoxia-ischemia neonatal brain injury by the inhibition of HIF-1alpha in a rat pup model.

Neuropharmacology·2015
Same author

Visible-Light-Dependent Photocyclization: Design, Synthesis, and Properties of a Cyanine-Based Dithienylethene.

The Journal of organic chemistry·2015
Same author

Salvianolic acid A inhibits endothelial dysfunction and vascular remodeling in spontaneously hypertensive rats.

Life sciences·2015
Same author

Application of nano TiO2 modified hollow fiber membranes in algal membrane bioreactors for high-density algae cultivation and wastewater polishing.

Bioresource technology·2015
Same author

Lead iodide perovskite light-emitting field-effect transistor.

Nature communications·2015
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
查看所有相关文章

相关实验视频

优化了具有深度学习的极端学习机器,用于高性能网络流量分类.

Xi Zhang1, Jun Yin2

  • 1School of Design, Jiangnan University, Wuxi, 214122, China.

Scientific reports
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种改进的极端学习机器 (IELM) 用于网络流量分类. 新的框架在检测恶意网络活动方面达到98.756%的准确性,提高了网络安全.

关键词:
极端学习机器 (ELM) 是一种极端学习机器.功能选择 功能选择网络安全 网络安全网络流量分类网络流量分类.粒子群集优化 (PSO) 是一种

相关实验视频

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:

  • 越来越多的网络用户和应用需要先进的网络安全解决方案.
  • 网络流量分析对于识别恶意活动和确保系统完整性至关重要.
  • 现有的方法需要改进,以提高网络流量的分类精度.

研究的目的:

  • 建议使用改进极端学习机器 (IELM) 进行网络流量分类的新框架.
  • 通过优化模型参数和优先考虑特征相关性来提高分类精度.
  • 为检测恶意活动和减轻安全风险提供强大且可扩展的解决方案.

主要方法:

  • 开发了一个改进的极端学习机器 (IELM) 框架,用于网络流量分类.
  • 集成的粒子群优化用于优化IELM模型参数.
  • 使用基于深度学习的特征选择机制来评估输入特征的相关性.

主要成果:

  • IELM框架在将网络流量分类为安全或不安全方面表现出高度准确性.
  • 在2017年CICIDS数据集上实现了98.756%的显著检测准确度.
  • 特征选择机制有效地优先考虑了相关的输入特征,提高了分类精度.

结论:

  • 提出的基于IELM的方法对于准确的网络流量分类非常有效.
  • 该框架在检测恶意活动和加强网络保护方面取得了重大进展.
  • 这些发现突显了IELM在强大和可扩展的网络安全解决方案中的潜力.