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相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Updated: Jul 23, 2025

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使用持续学习的终身网络鱼攻击检测.

Asif Ejaz1, Adnan Noor Mian2, Sanaullah Manzoor1

  • 1Department of Computer Science, Information Technology University, Lahore, 54000, Pakistan.

Scientific reports
|July 17, 2023
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概括
此摘要是机器生成的。

由于不断变化的威胁,传统的机器学习网络鱼检测随着时间的推移而失败. 持续学习 (CL) 模型保持高准确度,在持续的网络鱼检测中表现优于传统和转移学习方法.

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 网络鱼攻击对数字安全构成重大威胁,导致每天的凭证和资产丢失.
  • 传统的机器学习 (ML) 模型用于网络鱼检测的性能随着时间的推移而降低,这是由于不断变化的攻击载体和技术进步.
  • 网络鱼的动态性质需要适应性检测策略来保持有效性.

研究的目的:

  • 调查传统基于ML的网络鱼检测模型随着时间的推移而降低的性能.
  • 探索持续学习 (CL) 技术在维持网络鱼检测性能方面的有效性.
  • 将CL算法的性能与传统的ML和转移学习 (TL) 方法进行比较.

主要方法:

  • 从2018-2020年收集了网络鱼和良性样本,创建了六个不同的数据集.
  • 训练了使用CL方式对HTML内容进行深度特征嵌入的香草神经网络 (VNN).
  • 评估了拟议的CL算法与从头开始训练并使用TL的VNN模型相比.

主要成果:

  • 随着时间的推移,CL算法表现出持续的准确性,性能恶化仅为2.45%.
  • 从头开始训练的传统VNN模型经历了超过20.65%的性能下降.
  • 转移学习 (TL) 模型显示,绩效下降了8%.

结论:

  • 持续学习 (CL) 是一种高效的策略,可以保持强大的网络鱼检测性能,以应对不断变化的威胁.
  • 在长期的网络鱼检测准确性方面,CL显著优于传统的ML和转移学习方法.
  • 适应性学习方法对于网络安全防御来说至关重要,以跟上不断变化的网络攻击方法.