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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

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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...
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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Power System Three-Phase Short Circuits01:21

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Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
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频道特征和API基于频率的变压器模型用于恶意软件识别.

Liping Qian1, Lin Cong1

  • 1School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了CAFTrans,这是一种用于检测恶意软件 (恶意软件) 的新型变压器模型. CAFTrans 增强了 API 调用序列分析,提高了恶意软件识别准确度,以防止逃避技术.

关键词:
API序列的API序列是什么深度学习是一种深度学习.动态分析 动态分析恶意软件识别 恶意软件识别变压器的变压器是一个变压器.

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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:

  • 恶意软件 (恶意软件) 对信息安全构成持续威胁.
  • API调用序列对于恶意软件识别是有效的,但受到模糊化等逃避技术的挑战.
  • 现有的方法与复杂的API序列和微妙的恶意软件变体作斗争.

研究的目的:

  • 介绍CAFTrans,一种基于变压器的新型模型,用于增强恶意软件检测.
  • 提高识别恶意软件的准确性,包括未知的变体和对抗性攻击.
  • 解决当前处理复杂恶意软件逃避策略的方法的局限性.

主要方法:

  • 开发了CAFTrans,一个纳入一维通道注意模块 (1D-CAM) 的变压器模型,用于改进API调用向量特征相关性.
  • 实现了一个单词频率增强模块,以保留关键的低频API功能.
  • 集成卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络,以捕捉复杂的API关系.

主要成果:

  • 在mal-api-2019数据集上,CAFTrans取得了最先进的表现.
  • 该模型获得了F1得分为0.65252和曲线下的面积 (AUC) 为0.8913.
  • 在区分恶意软件类型和识别未知的样本方面表现出卓越的准确性.

结论:

  • CAFTrans通过完善API功能嵌入和捕捉复杂的关系,显著提高恶意软件检测的准确性.
  • 该模型显示了在识别各种恶意软件方面改进的能力,包括零日威胁和对抗样本.
  • 调查结果强调了基于变压器的架构的潜力,这些架构具有强大的网络安全解决方案的注意力机制.