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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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基于机器学习的入侵检测框架用于检测物联网中的安全攻击.

V Kantharaju1, H Suresh2, M Niranjanamurthy1

  • 1Deparment of AI&ML, BMS Institute of Technology and Management (Affiliated to Visvesvaraya Technological University, Belagavi), Bengaluru, India.

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概括

本研究介绍了一种自我注意力进步生成对抗网络 (SAPGAN),用于先进的物联网 (IoT) 安全性. SAPGAN提高了入侵检测的准确性,并减少了识别物联网网络威胁的计算时间.

关键词:
数据采集 数据采集物联网的物联网,就是物联网.侵入检测入侵检测系统可以检测入侵者.安全的安全的安全的安全的安全.在WSOA中,WSOA是WSOA.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 涉及相互连接的设备交换数据,需要强有力的安全措施.
  • 传统的基于深度学习的物联网入侵检测系统 (IDS) 面临着准确的攻击分类和高计算需求的挑战.
  • 现有的IDS方法很难在复杂的物联网环境中高效准确地检测各种威胁.

研究的目的:

  • 提出一个先进的入侵检测系统 (IDS) 框架,即自我注意力进步生成对抗网络 (SAPGAN),以提高物联网网络的安全性.
  • 解决传统深度学习IDS在物联网威胁检测的准确性和计算效率方面的局限性.
  • 开发一种能够准确分类入侵并优化功能选择以改善物联网网络安全的新框架.

主要方法:

  • 使用本地最小平方来处理缺失值的数据收集和预处理.
  • 使用修改后的战争战略优化算法 (WSOA) 来识别最佳特征的特征选择.
  • 实施SAPGAN框架,将网络流量分为"异常"和"正常"类别,包括基于摄像头的洪水和DDoS等攻击.

主要成果:

  • 与最先进的模型相比,拟议的SAPGAN框架实现了更高的准确性,改进了23.19%,27.55%和18.35%.
  • SAPGAN显示了显著降低的计算时间,超过传统模型的14.46%,26.76%和13.65%.
  • 该框架有效地将入侵者分为"异常"和"正常"的类别,基于从WSOA获得的优化特征.

结论:

  • SAPGAN框架为物联网入侵检测提供了卓越的解决方案,在准确性和效率上都超过了传统方法.
  • 自我注意机制和生成对抗网络的集成为识别物联网中复杂的网络威胁提供了强大的方法.
  • 这项研究为计算效率高,高度准确的IDS做出了贡献,这对于确保物联网快速扩展的景观至关重要.