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相关概念视频

Chunking and Rehearsal in Sensory Memory01:22

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Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
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Interference and Decay01:16

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Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
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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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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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一个改进的长期短期内存网络用于入侵检测检测.

Asmaa Ahmed Awad1, Ahmed Fouad Ali1,2, Tarek Gaber3,1

  • 1Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.

PloS one
|August 1, 2023
PubMed
概括

一个新的改进的长期短期记忆 (ILSTM) 算法增强了入侵检测系统. 与传统方法相比,这种新的方法显著提高了识别网络威胁的准确性和精确性.

科学领域:

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

背景情况:

  • 侵入检测系统 (IDS) 对网络安全至关重要,可以识别来自网络流量的威胁.
  • 传统的机器学习方法在IDS中经常与低准确度和高错误报警率作斗争.
  • 像长期短期记忆 (LSTM) 这样的深度学习模型显示出希望,但需要广泛的培训.

研究的目的:

  • 提出一种新的改进的长期短期记忆 (ILSTM) 算法,用于增强入侵检测.
  • 提高网络入侵检测系统的准确性和减少虚假报警.
  • 开发一种高效的IDS,能够处理网络攻击的二进制和多类分类.

主要方法:

  • 开发了一个ILSTM算法,集成混乱蝶优化算法 (CBOA) 和粒子群优化 (PSO).
  • 采用了两阶段的方法:最初的LSTM培训,然后是CBOA和PSO以优化体重.
  • 在使用九个性能指标的NSL-KDD和LITNET-2020数据集上评估了基于ILSTM的IDS.

主要成果:

  • 与标准的LSTM相比,ILSTM算法获得了明显更高的准确性 (93.09%) 和精度 (96.86%) (82.74%的准确性,76.49%的精度).
  • 在两种数据集中,ILSTM在LSTM和其他深度学习算法上都表现出卓越的性能.

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  • 统计分析证实了ILSTM的更大意义,特别是在多种类型的入侵类型的多重分类中,如DoS,Prob和U2R.
  • 结论:

    • 拟议的ILSTM算法为网络入侵检测系统提供了实质性的改进.
    • ILSTM有效地提高了准确性和精度,优于现有的深度学习模型.
    • 这种优化的方法为识别各种网络入侵提供了更强大,更具统计意义的解决方案.