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相关概念视频

Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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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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Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
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ConLBS:一种使用对比学习与行为序列的攻击调查方法.

Jiawei Li1, Ru Zhang1, Jianyi Liu1

  • 1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
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概括
此摘要是机器生成的。

这项研究介绍了ConLBS,这是一种在法医中进行攻击调查的新方法. ConLBS有效地使用对比学习和变压器网络识别伪装攻击,即使具有有限的标记数据.

关键词:
袭击调查调查调查攻击审计日志 审计日志行为序列 行为序列.相反的学习学习学习.

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科学领域:

  • 网络安全 网络安全
  • 计算机法医学 计算机法医学
  • 机器学习 机器学习

背景情况:

  • 在法医分析中,有效的攻击调查至关重要.
  • 监督方法需要大量的标记数据,而这些数据往往很少.
  • 无监督的方法由于与正常行为相似,因此与伪装攻击作斗争.

研究的目的:

  • 提出ConLBS,一种结合对比学习和多层变压器进行行为序列分类的方法.
  • 为了增强数字取证中伪装攻击的识别.
  • 根据数据的可用性,实现灵活的培训 (监督或无监督).

主要方法:

  • 从审计日志构建行为序列来描述模式.
  • 采用一种新的 lemmatization 策略,将语义映射到攻击模式层.
  • 使用对比学习和多层变压器网络.
  • 探索四种增强策略,以区分攻击和正常行为序列.
  • 在未标记的序列上执行无监督的表示学习.

主要成果:

  • ConLBS在识别未标记或有限的标记数据的攻击行为序列方面表现出有效性.
  • 该方法在攻击调查中与现有的方法和模型相比,实现了更高的性能.
  • 在两个公共数据集上进行评估,ConLBS显示出强大的攻击检测能力.

结论:

  • 对于攻击调查而言,ConLBS提供了一个强大的解决方案,特别是在数据稀缺的情况下.
  • 对比学习和变压器网络的组合显著改善了对复杂攻击的检测.
  • ConLBS提供了一个多功能和有效的工具,用于数字取证分析.