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相关概念视频

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Flowers are the reproductive, seed-producing structures of angiosperms. Typically, flowers consist of sepals, petals, stamens, and carpels. Sepals and petals are the vegetative flower organs. Stamens and carpels are the reproductive organs.  
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使用包装方法对不平衡数据进行最佳入侵检测,使用深度神经网络进行包装,并通过花花授粉算法进行优化.

Hussein Ridha Sayegh1, Wang Dong1, Bahaa Hussein Taher1

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China.

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概括
此摘要是机器生成的。

本研究介绍了一种用于物联网 (IoT) 网络的新型入侵检测系统 (IDS),该系统使用混合的元启发和深度学习方法. 新系统有效地检测网络入侵,并解决安全数据集中的类不平衡问题.

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包装分类器包装分类器班级的重量是不同的.深度神经网络 (DNN) 是一个深度神经网络.花花授粉算法 (FPA) 是一种花花授粉算法.不平衡类的分布是不平衡类.物联网 (IoT) 的物联网 (IoT) 的物联网.入侵检测系统 (IDS) 是一种入侵检测系统.

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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:

  • 物联网 (IoT) 设备的扩散需要强有力的安全措施.
  • 侵入检测系统 (IDS) 对于识别物联网网络中的恶意活动至关重要.
  • 侵入数据集中的阶级不平衡对IDS开发构成了重大挑战.

研究的目的:

  • 为物联网网络提出一种新的混合IDS.
  • 为了提高入侵检测的准确性和处理类不平衡.
  • 为了提高网络安全,利用元启发式和深度学习技术.

主要方法:

  • 这是一种混合方法,结合了花花授粉算法 (FPA) 和深度神经网络 (DNN).
  • 一种使用大致平衡 (RB) 包装策略的集体学习范式.
  • 经过FPA培训的DNN具有成本敏感的适应性功能,可以作为基础学习者进行公正的模型培训.

主要成果:

  • 拟议的IDS在四个基准数据集 (NSL-KDD,UNSW NB-15,CIC-IDS-2017,BoT-IoT) 中表现出卓越的性能.
  • 通过RB Bagging战略,实现了对阶级不平衡的有效处理.
  • 报告了高准确度,精度,回忆和F1得分,表现优于现有的IDS.

结论:

  • 混合FPA-DNN IDS提供了一种有效的解决方案,用于检测物联网环境中的入侵.
  • 瑞银包装策略成功地减轻了不平衡数据集的挑战.
  • 这种方法在保护物联网网络免受网络威胁方面取得了重大进展.