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相关概念视频

Classification of Systems-II01:31

Classification of Systems-II

242
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,
242
Classification of Systems-I01:26

Classification of Systems-I

319
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:
319
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

152
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...
152

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相关实验视频

Updated: Sep 17, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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在汽车互联网 (IoV) 中使用基于深度学习的可解释的人工智能与优化算法的两阶段恶意软件检测模型.

Manal Abdullah Alohali1, Sultan Alahmari2, Mohammed Aljebreen3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Scientific reports
|July 2, 2025
PubMed
概括

本研究介绍了一种新的AI模型,用于检测汽车互联网 (IoV) 中的恶意软件. MDMIoV-DLXAI模型显著提高了恶意软件检测的准确性,提高了车辆网络安全性.

关键词:
深度学习是一种深度学习.可解释的人工智能功能选择 功能选择车辆的互联网汽车的互联网恶意软件检测检测 恶意软件检测

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 智能运输系统 智能运输系统

背景情况:

  • 由于恶意软件威胁的增加,汽车互联网 (IoV) 面临着重大的隐私和安全挑战.
  • 恶意软件可能导致数据盗窃,腐败和网络犯罪,影响网络运营和用户安全.
  • 现有的恶意软件检测解决方案通常需要提高速度和准确性.

研究的目的:

  • 提出一种新的基于深度学习的可解释的人工智能模型 (MDMIoV-DLXAI),用于在IoV环境中增强恶意软件检测和分类.
  • 提高连接汽车中的恶意软件检测系统的准确性和效率.
  • 利用可解释的人工智能 (XAI) 来提高人工智能驱动的安全决策.

主要方法:

  • 使用min-max规范化的数据规范化.
  • 使用爬行动物搜索算法 (RSA) 进行特征选择.
  • 恶意软件分类使用混合双向长短期内存与多头自我注意 (BiLSTM-MHSA) 模型,优化了客优化算法 (POA).
  • 可解释AI (XAI) 使用SHAP进行增强决策.

主要成果:

  • 拟议的MDMIoV-DLXAI模型在恶意软件检测方面实现了97.393%的卓越准确性.
  • 结合DL模型,优化算法和XAI技术,显著提高了性能.
  • 实验评估证实了该模型与现有技术相比的有效性.

结论:

  • MDMIoV-DLXAI模型为IoV网络中的恶意软件检测提供了一个强大而准确的解决方案.
  • 可解释的人工智能提高了车辆中人工智能驱动的安全系统的可信性和可解释性.
  • 这项研究有助于保护智能运输系统免受不断变化的网络威胁.