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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

190
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:
190
Classification of Systems-II01:31

Classification of Systems-II

150
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,
150
Classification of Signals01:30

Classification of Signals

482
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...
482
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
Aggregates Classification01:29

Aggregates Classification

328
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...
328
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

109
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...
109

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相关实验视频

Updated: Jul 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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通过人工智能推进网络安全:用于入侵检测的基于SVM的深度学习.

Khadija M Abuali1, Liyth Nissirat1, Aida Al-Samawi1

  • 1Department of Computer Networks, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
概括

本研究介绍了一种使用支持矢量机器 (SVM) 深度学习的新型入侵检测系统 (IDS). 该系统在识别社交媒体网络入侵时达到100%的准确性.

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 由于社交媒体的增长和互联网的可访问性,企业面临越来越多的网络威胁.
  • 侵入检测系统 (IDS) 对网络安全至关重要,分析流量以识别恶意活动.
  • 进化的攻击向量和网络复杂性需要先进的IDS解决方案,包括人工智能驱动的方法.

研究的目的:

  • 提出基于支持矢量机器 (SVM) 的深度学习系统,用于社交媒体网络上的入侵检测.
  • 为了分类服务器提取的数据,以识别入侵事件.
  • 用CSE-CIC-IDS 2018数据集来评估系统的有效性.

主要方法:

  • 开发了一个使用支持矢量机 (SVM) 的深度学习系统.
  • 在系统评估中使用了CSE-CIC-IDS 2018数据集.
  • 在对100,000个实例训练模型之前,对数据集应用了数据预处理技术.

主要成果:

  • 拟议的基于SVM的深度学习IDS在关键指标上取得了完美的成绩.
  • 准确性,真正回忆,精度,特异性和F-score都记录在100%.
  • 假阳性回忆率为0%,表明正常流量没有被错误地归类为恶意.
关键词:
在2018年,CIC-IDS将在2018年推出CIC-IDS.深度学习是一种深度学习.侵入检测系统的入侵检测系统多类分类是多类分类的分类.支持矢量机器支持矢量机器

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结论:

  • 开发的深度学习IDS在检测社交媒体入侵方面表现出卓越的表现.
  • 基于SVM的方法有效地分类网络流量,提供强大的安全性.
  • 该系统为现代网络安全挑战提供了高度准确和可靠的解决方案.