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

Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...

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

A UAV Testbed for Diagnosing Hardware Vulnerabilities: Quantifying Sim-to-Real Discrepancies in PX4 Flight Logs.

Sensors (Basel, Switzerland)·2026
Same author

From Concrete to Code: A Survey of AI-Driven Transportation Infrastructure, Security, and Human Interaction.

Sensors (Basel, Switzerland)·2026
Same author

FedECPA: An Efficient Countermeasure Against Scaling-Based Model Poisoning Attacks in Blockchain-Based Federated Learning.

Sensors (Basel, Switzerland)·2025
Same author

Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning.

Sensors (Basel, Switzerland)·2025
Same author

Real-Time Anomaly Detection in Physiological Parameters: A Multi-Squad Monitoring and Communication Architecture.

Sensors (Basel, Switzerland)·2025
Same author

CANGuard: An Enhanced Approach to the Detection of Anomalies in CAN-Enabled Vehicles.

Sensors (Basel, Switzerland)·2025

相关实验视频

Updated: Jun 26, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.2K

使用机器学习检测恶意威胁,利用时钟门硬件.

Nuri Alperen Kose1, Razaq Jinad1, Amar Rasheed1

  • 1Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括

本研究介绍了入侵检测系统 (IDS),用于打击针对ARM Cortex-M微控制器的时钟门恶意软件. K-Nearest分类器和物流回归IDS实现了高检测率,提高了嵌入式系统的安全性.

关键词:
在 ARM 皮层.嵌入式系统 嵌入式系统检测入侵 检测入侵机器学习是机器学习.恶意软件 恶意软件 恶意软件

更多相关视频

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.7K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jun 26, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.2K
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

3.7K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 嵌入式系统工程 嵌入式系统工程

背景情况:

  • 嵌入式系统在关键基础设施中至关重要,但缺乏固有的安全性,使他们容易受到网络攻击.
  • 时钟控制恶意软件利用ARM Cortex-M微控制器中的硬件漏洞,破坏了系统的可靠性.
  • 这种威胁需要嵌入式平台的先进安全解决方案.

研究的目的:

  • 开发和评估专门用于检测时钟门恶意软件的入侵检测系统 (IDS).
  • 评估各种机器学习算法的有效性,以识别和分类恶意软件变体.
  • 为了提高基于ARM Cortex-M的嵌入式系统的安全性和可靠性.

主要方法:

  • 实施和比较六种不同的IDS方法:K-最近分类器,随机森林,物流回归,决策树,天真湾和随机梯度下降.
  • 使用正常运行和恶意软件攻击场景中的功耗数据对IDS进行培训和验证.
  • 对各种时钟门恶意软件注入代码的检测准确度的分析.

主要成果:

  • 开发的IDS在检测时钟门恶意软件方面表现出了显著的能力.
  • 基于K-最近分类器和物流回归的IDS实现了特别高的检测率,达到0.99.
  • 该研究证实了电力消耗分析在恶意软件检测方面的有效性.

结论:

  • 拟议的入侵检测系统为ARM Cortex-M嵌入式系统中的时钟门恶意软件提供了强大的防御.
  • 机器学习方法,特别是K-Nearest分类器和后勤回归,对于识别复杂的硬件级威胁是有效的.
  • 实施这些IDS对于保护依赖嵌入式技术的关键基础设施至关重要.