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相关概念视频

Classification of Signals01:30

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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.
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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.
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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Systems-II01:31

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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,
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TSFN:一种使用BERT和LSTM的新型恶意流量分类方法.

Zhaolei Shi1, Nurbol Luktarhan1, Yangyang Song1

  • 1College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
概括

本研究引入了一种基于BERT的新型时间序列特征网络 (TSFN),用于增强恶意流量分类. 通过整合全球和时间序列功能,TSFN模型显著提高了网络安全和异常检测准确度.

科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 交通分类对于网络异常检测和安全至关重要.
  • 现有的方法,如统计和深度学习方法,在特征工程和数据依赖方面存在局限性.
  • 目前基于BERT的方法忽略了时间序列的基本流量特征.

研究的目的:

  • 提出一种基于BERT的新型时间序列特征网络 (TSFN) 模型.
  • 解决现有的恶意流量分类方法的局限性.
  • 为了提高检测恶意网络流量的准确性.

主要方法:

  • 基于BERT模型的包编码器使用注意力机制捕获全球流量特征.
  • 一个长短期记忆 (LSTM) 模型从交通中提取时间特征.
  • 该TSFN模型整合了全球和时间序列特征,以提供全面的表示.

主要成果:

  • 该TSFN模型有效地捕捉了网络流量的全球和时间序列特征.
  • 关于USTC-TFC数据集的实验结果表明,分类准确度有了显著的改善.
  • 获得了99.50%的F1得分,这表明恶意流量分类中的高性能.
关键词:
来自变压器的双向编码器表示长期短期记忆 长期短期记忆恶意流量分类恶意流量的分类预先培训的培训前培训

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结论:

  • 整合时间序列功能显著提高了恶意流量分类的准确性.
  • 拟议的基于BERT的TSFN模型为网络安全提供了一个强大的解决方案.
  • 这种方法为网络异常检测提供了更有效的方法.