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 Systems-I01:26

Classification of Systems-I

186
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:
186
Classification of Systems-II01:31

Classification of Systems-II

146
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,
146
Classification of Signals01:30

Classification of Signals

462
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...
462
Aggregates Classification01:29

Aggregates Classification

326
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...
326
Force Classification01:22

Force Classification

1.2K
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,...
1.2K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106

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

Distributed fuzzy clustering approach for balanced energy consumption in large-scale networks.

Scientific reports·2026
Same author

Bridging modalities: a deep learning framework for brain tumor classification via CT-MRI integration and model fusion.

Frontiers in computational neuroscience·2026
Same author

Retraction Note: Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach.

Scientific reports·2026
Same author

Integrative multi-stage deep learning framework for ovarian tumor ultrasound classification with explainability and confidence estimation.

Frontiers in medicine·2026
Same author

Quantum transfer learning for cross-domain cybersecurity threat detection and categorization.

Scientific reports·2026
Same author

A multimodal learning and simulation approach for perception in autonomous driving systems.

Scientific reports·2026

相关实验视频

Updated: Jul 5, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

评估深度学习变体用于网络攻击检测和物联网网络中的多类分类.

Sidra Abbas1, Imen Bouazzi2, Stephen Ojo3

  • 1Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan.

PeerJ. Computer science
|January 23, 2024
PubMed
概括

这项研究使用深度学习模型来检测物联网 (IoT) 中的网络攻击. 循环神经网络 (RNN) 模型实现了96.56%的准确性,显示了识别威胁的效率.

关键词:
网络攻击就是网络攻击.对于DDoS攻击来说,这是一次性攻击.深度学习是一种深度学习.这就是为什么物联网是物联网物联网.

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

760
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

相关实验视频

Last Updated: Jul 5, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

760
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

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) 正在迅速扩展,增加了它对复杂网络攻击的脆弱性.
  • 网络攻击对企业和组织构成重大威胁,影响运营和数据安全.
  • 机器学习 (ML) 和深度学习 (DL) 为增强网络安全防御提供了有希望的解决方案.

研究的目的:

  • 调查深度学习模型在物联网环境中检测网络攻击的有效性.
  • 评估各种深度学习架构,以识别网络流量异常.
  • 为早期网络数据隔离和网络攻击缓解提出一种有效的方法.

主要方法:

  • 利用CICDIoT2023数据集来评估深度学习模型.
  • 实现数据预处理,包括对分类变量进行强大的标量和标签编码.
  • 应用深度学习模型,如深度神经网络 (DNN),卷积神经网络 (CNN) 和循环神经网络 (RNN),用于网络攻击的检测.

主要成果:

  • 循环神经网络 (RNN) 模型以96.56%的准确度显示出最高的准确度.
  • 拟议的方法,利用深度学习,在确定现实的物联网设置中的网络攻击方面被证明是有效的.
  • 实验结果证实了在网络流量分析中实施的深度学习模型的有效性.

结论:

  • 深度学习模型,特别是RNN,对于检测物联网网络中的网络攻击非常有效.
  • 拟议的方法为缓解物联网不断变化的环境中的网络威胁提供了有效的解决方案.
  • 使用深度学习的早期检测和数据分离对于强大的物联网安全至关重要.