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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

98
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
98
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

140
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
140
Stereotype Content Model02:16

Stereotype Content Model

14.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.0K
Types Of Transformers01:16

Types Of Transformers

949
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
949
Energy Losses in Transformers01:21

Energy Losses in Transformers

836
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
836
The Ideal Transformer01:26

The Ideal Transformer

356
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
356

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Updated: Jun 9, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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ViTDroid:视觉转换器,以高效,可解释地关注Android二进制文件中的恶意行为.

Toqeer Ali Syed1, Mohammad Nauman2, Sohail Khan2

  • 1Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括
此摘要是机器生成的。

新的深度学习模型ViTDroid通过识别恶意代码指令来分析Android恶意软件. 这种可解释的AI方法有助于恶意软件检测和分析的专家,提高了对不断增加的移动威胁的安全性.

关键词:
一个安卓的安卓安卓安卓.恶意软件 恶意软件 恶意软件安全的安全的安全的安全的安全.视觉变压器 视觉变压器

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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:

  • 安卓主导移动操作系统市场 (71%的份额),使其用户容易受到越来越多的移动恶意软件威胁.
  • 传统的恶意软件分析很难跟上移动恶意软件不断增长的数量和复杂性.
  • 深度学习模型虽然在图像分析中有效,但在解释恶意软件特征方面面临挑战,并且存在诸如CNN中的翻译不变性等局限性.

研究的目的:

  • 介绍ViTDroid,一个新的深度学习模型,利用视觉转换器来分析Android恶意软件的opcode序列.
  • 提高恶意软件分析中的深度学习模型的可解释性,超越简单的分类.
  • 为Android恶意软件样本中导致恶意行为的特定指令提供可操作的见解.

主要方法:

  • 开发了ViTDroid,这是一个基于视觉转换器的深度学习模型,用于分析Android恶意软件的opcode序列.
  • 在大型的,真实世界的Android恶意软件样本数据集上训练和评估模型.
  • 专注于通过识别恶意行为引起的指令来实现可解释的预测.

主要成果:

  • 实现了0.0019的低虚假阳性率,超过了之前的0.0021.
  • 证明了该模型不仅能够准确地对恶意软件进行分类,而且还能够精确地确定特定的恶意指令.
  • 为恶意软件分类背后的原因提供了可解释的见解.

结论:

  • 通过可解释的预测,ViTDroid在基于深度学习的Android恶意软件分析方面取得了重大进展.
  • 该模型识别恶意指令的能力有助于人类专家,提高了整体恶意软件分析过程.
  • ViTDroid通过提供对恶意软件行为更深入的见解,并促进更有效的威胁检测,为提高网络安全做出了贡献.