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相关概念视频

Methods of Classification and Identification01:28

Methods of Classification and Identification

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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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A modern form of aggression is bullying. As you learn in your study of child development, socializing and playing with other children is beneficial for children’s psychological development. However, as you may have experienced as a child, not all play behavior has positive outcomes. Some children are aggressive and want to play roughly. Other children are selfish and do not want to share toys. One form of negative social interactions among children that has become a national concern is...
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相关实验视频

Updated: Jul 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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基于BERT的方法来识别恶意URL.

Ming-Yang Su1, Kuan-Lin Su1

  • 1Department of Computer Science and Information Engineering, Ming Chuan University, Taoyuan City 333, Taiwan.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于BERT的模型,用于高精度检测恶意URL. 该系统有效地识别有害的网址,增强网络安全防御针对网络鱼和恶意软件.

关键词:
贝尔特 (BERT) 公司卫生部 卫生部 卫生部 卫生部这就是为什么物联网物联网物联网.有恶意的URL.网络鱼 (phishing) 是一种网络鱼.

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 自然语言处理自然语言处理.

背景情况:

  • 恶意统一资源定位器 (URL) 在网络攻击中构成重大威胁,包括网络鱼和恶意软件分发.
  • 准确检测恶意URL对于强大的网络安全措施至关重要.
  • 之前的研究利用了深度学习模型进行URL分析,经常将字符串细分成用于分类的令牌.

研究的目的:

  • 使用基于变压器的模型开发和评估一种用于恶意URL检测的新方法.
  • 利用BERT的自我注意机制,以更好地理解URL字符串中的标记相关性.
  • 评估模型在各种数据集中的性能,包括物联网和卫生部等专业领域.

主要方法:

  • 一个来自变压器的双向编码器表示 (BERT) 模型被用于标记URL字符串.
  • 用BERT内部的自我注意机制来捕捉代币之间的关系.
  • 集成了一个分类层,以区分恶意和良性URL.
  • 该模型在三个公共数据集 (Kaggle,GitHub,ISCX 2016) 和两个域特定数据集 (IoT,DoH) 上进行了评估.

主要成果:

  • 拟议的基于BERT的系统在三个主要的公共数据集上实现了98.78%,96.71%和99.98%的高准确率.
  • 该模型在物联网 (IoT) 和域名系统在HTTPS (DoH) 数据集上测试时展示了多功能性和有效性.
  • 自注意机制在提高模型对URL结构的理解方面被证明是有效的.

结论:

  • 开发的基于BERT的模型为恶意URL检测提供了一个高度准确和多功能解决方案.
  • 这种方法显著提高了自动化系统在识别和减轻网络威胁方面的能力.
  • 该模型在各种数据集中的强性能突出显示了它对现实世界网络安全应用的潜力.