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相关概念视频

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
Introduction to Learning01:18

Introduction to Learning

357
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
357
Observational Learning01:12

Observational Learning

158
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
158
Classification of Systems-I01:26

Classification of Systems-I

178
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:
178
Associative Learning01:27

Associative Learning

324
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
324
Classification of Systems-II01:31

Classification of Systems-II

138
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,
138

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相关实验视频

Updated: Jun 18, 2025

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

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BFLIDS:用于IoMT网络入侵检测的区块链驱动的联合学习.

Khadija Begum1, Md Ariful Islam Mozumder1, Moon-Il Joo1

  • 1Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
概括

本研究介绍了BFLIDS,这是一个使用区块链和联合学习的新系统,用于在医疗物联网网络中安全检测入侵. 它在不集中敏感信息的情况下增强网络安全和数据隐私.

科学领域:

  • 网络安全 网络安全
  • 医疗保健技术 技术 医疗保健 技术
  • 机器学习 机器学习

背景情况:

  • 医疗物联网 (IoMT) 存在重大安全漏洞.
  • 传统的安全措施对于动态的IoMT环境是不够的.
  • 集中式机器学习入侵检测系统 (IDS) 由于单点故障,引发了隐私问题.

研究的目的:

  • 为IoMT网络开发一个安全和保护隐私的IDS.
  • 通过使用一种新的框架来增强入侵检测能力.
  • 解决IOMT安全中集中式机器学习的局限性.

主要方法:

  • 引入基于区块链授权的基于联邦学习的IDS (BFLIDS).
  • 集成区块链来实现交易安全,联合学习来实现数据隐私,IPFS用于去中心化存储,MongoDB用于数据管理.
  • 用Kullback-Leibler分歧和自适应加权对FedAvg算法的修改.
  • 实现基于Adaptive Max Pooling的CNN和修改后的BiLSTM,注意分类.

主要成果:

  • 实现了高精度:97.43% (CNNs/Edge-IIoTSet),96.02% (BiLSTM/Edge-IIoTSet),98.21% (CNNs/TON-IoT) 和97.42% (BiLSTM/TON-IoT) 在联合学习场景中.
关键词:
医疗事物的互联网 (IoMT)区块链区块链区块链区块链区块链联合学习的联合学习侵入检测系统的入侵检测系统隐私 隐私 隐私 隐私 隐私 隐私安全的安全的安全的安全的安全.

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  • 与集中式方法相比,已经证明了竞争性表现.
  • 验证了BFLIDS在检测IoMT网络入侵方面的有效性.
  • 结论:

    • BFLIDS有效地提高了IoMT网络中的安全性和隐私性.
    • 拟议的系统提供了一个强大的解决方案,用于在资源有限和敏感的环境中进行入侵检测.
    • 区块链和联合学习集成为IoMT网络安全提供了可扩展和安全的方法.