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Classification of Systems-I01:26

Classification of Systems-I

179
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:
179
Classification of Systems-II01:31

Classification of Systems-II

139
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,
139
Classification of Signals01:30

Classification of Signals

432
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...
432
Aggregates Classification01:29

Aggregates Classification

314
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...
314
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Force Classification01:22

Force Classification

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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,...
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相关实验视频

Updated: Jun 21, 2025

Design and Analysis for Fall Detection System Simplification
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优化物联网入侵检测使用平衡类分布,特征选择和集成机器学习技术.

Muhammad Bisri Musthafa1, Samsul Huda2, Yuta Kodera1

  • 1Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括

为物联网 (IoT) 优化入侵检测系统 (IDS) 对网络安全至关重要. 这项研究通过使用类平衡和特征选择来提高IDS性能,LSTM堆叠在检测网络攻击方面实现了卓越的准确性.

关键词:
班级平衡是一种平衡.组合技巧 组合技巧 组合技巧功能选择 功能选择侵入检测系统的入侵检测系统堆叠长时间的短期记忆.

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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) 设备的扩散需要强大的入侵检测系统 (IDS) 来增强网络安全.
  • 传统的IDS方法与新的威胁作斗争,突出了需要先进的技术,如机器学习 (ML) 和深度学习 (DL).
  • 在IDS中的ML和DL模型面临着诸如过度装配和不相关特征的影响等挑战,从而损害了它们的有效性.

研究的目的:

  • 通过解决ML模型的局限性,优化物联网环境中的入侵检测.
  • 改进新型和复杂网络攻击的检测.
  • 通过有效的预处理技术,提高IDS的可靠性和性能.

主要方法:

  • 实施了预处理方案,包括类平衡和特征选择,以优化ML模型.
  • 评估了两个整体模型:支持向量机 (SVM) 带有袋装和长短期内存 (LSTM) 带有堆叠.
  • 用UNSW-NB15和NSL-KD数据集进行实验评估.

主要成果:

  • 具有差异分析 (ANOVA) 功能选择的LSTM堆叠模型在分类网络攻击方面表现出卓越的性能.
  • 在UNSW-NB15和NSL-KD数据集上分别达到96.92%和99.77%的高精度.
  • 报告了最小的过,值为0.33%和0.04%,以及高的曲线下面积 (AUC) 值为0.9665和0.9971.

结论:

  • 拟议的优化方案,特别是LSTM堆叠与ANOVA特征选择,显著提高物联网入侵检测能力.
  • 该模型有效地减轻了过度装配,并改善了对各种网络攻击的检测.
  • 这种方法为不断扩大的互联物联网设备领域提供了更具弹性和准确的网络安全解决方案.