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

Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

1.8K
Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
1.8K

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

Multi-dimensional laboratory characterization: VOCs emission and evolution in warm-mix synchronous rejuvenated (WMA-SR) asphalt pavement throughout construction stages.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

From Pre-Swelling to Performance Enhancement: Mechanisms and Effects of an Instant Ultra High-Performance Bituminous Material Modifier.

Materials (Basel, Switzerland)·2026
Same author

Phosphogypsum and carbide slag synergy for red mud soil stabilization: Mechanical performance, environmental impacts, and micro-scale mechanisms.

Environmental research·2025
Same author

Quantitative multifractal demarcation of bitumen colloidal architecture: Resolving characteristic dimensions and structural regimes in multiscale hierarchies.

Journal of colloid and interface science·2025
Same author

Visible-Light-Induced Tandem Nickel-Catalyzed Heck Cyclization/Self-Promoted [2+2] Intermolecular Cycloaddition.

Organic letters·2025
Same author

Molecular Dynamics Investigation of the Diffusion Mechanisms and Thermodynamic Behaviors in Warm Mix Recycled Asphalt Binders with and Without Rejuvenators.

Materials (Basel, Switzerland)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Apr 29, 2026

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.1K

CSMC:一种安全有效的可视化恶意软件分类方法,灵感来自压缩传感.

Wei Wu1,2, Haipeng Peng1,2, Haotian Zhu1,2

  • 1Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了压缩感应恶意软件分类 (CSMC) 以有效地识别物联网 (IoT) 环境中的恶意软件. CSMC使用深度学习压缩恶意软件样本,增强安全性并提高Windows和Android系统的分类准确性.

关键词:
压力感应感应 压力感应感应卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.家庭分类的家庭分类.

更多相关视频

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

1.4K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

相关实验视频

Last Updated: Apr 29, 2026

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.1K
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

1.4K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K

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:

  • 物联网中的智能传感器至关重要,但容易受到复杂的恶意软件攻击.
  • 恶意软件的分类至关重要,但由于庞大的样本大小和有限的物联网资源,这是一个挑战.
  • 现有的方法在复杂网络中的高效处理,安全共享和强大的分类方面扎.

研究的目的:

  • 为物联网环境提出一种高效和安全的恶意软件分类方法.
  • 为了应对有限的带宽,资源限制和样本利用风险的挑战.
  • 为了提高恶意软件分类的准确性和针对复杂网络威胁的稳定性.

主要方法:

  • 开发了压缩传感恶意软件分类 (CSMC),集成压缩传感和深度学习.
  • 在共享和分类之前实施恶意软件样本压缩.
  • 利用深度学习在压缩过程中提取特征,确保不可逆转性以提高安全性.

主要成果:

  • 与Windows和Android恶意软件的压缩传感和机器/深度学习方法相比,CSMC表现出优异的性能.
  • 实验证实了CSMC在安全样本共享和处理方面的有效性.
  • 通过样本重建和噪音处理实验验证实了强度.

结论:

  • CSMC为物联网中的恶意软件分类提供了一个高效,安全和强大的解决方案.
  • 深度学习在压缩传感中集成,使先进的功能提取和保护成为可能.
  • CSMC显著提高了对资源有限和安全敏感物联网应用程序的恶意软件识别能力.