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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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一个基于LSTM的优化深度学习模型,用于异常网络入侵检测.

Nitu Dash1, Sujata Chakravarty2, Amiya Kumar Rath3

  • 1Department of Computer Science and Engineering, BPUT, Rourkela, Odisha, India.

Scientific reports
|January 9, 2025
PubMed
概括

本研究引入了针对网络入侵检测的优化深度学习模型. 通过Salp Swarm算法优化的长期短期内存 (SSA-LSTM) 模型在识别跨多个数据集的网络流量异常方面表现出卓越的性能.

关键词:
入侵检测系统 (IDS) 是一种入侵检测系统.优化JAYA的优化方法长时间的短期记忆 (LSTM)粒子群集优化 (PSO) 是一种萨尔普群集算法 (SSA) 是一种

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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:

  • 越来越多的网络连接需要强大的网络安全和网络攻击防御.
  • 侵入检测系统 (IDS) 对于识别网络漏洞至关重要,但传统方法往往具有较高的错误报警率.
  • 机器学习和深度学习为提高IDS有效性提供了有希望的解决方案.

研究的目的:

  • 为准确的网络流量异常检测提出一个优化的长短期内存 (LSTM) 模型.
  • 为了减轻与传统入侵检测系统相关的高错误报警率.
  • 评估深度学习方法在网络安全应用中的有效性.

主要方法:

  • 一个优化的长短期内存 (LSTM) 网络被开发用于网络流量中的异常识别.
  • 三种优化算法 - - 粒子群优化 (PSO),JAYA和Salp群算法 (SSA) - - 用于调整LSTM的超参数.
  • 建议的模型使用NSL KDD,CICIDS和BoT-IoT数据集进行了评估.

主要成果:

  • 对PSO-LSTMIDS,JAYA-LSTMIDS和SSA-LSTMIDS进行了比较分析.
  • 通过Salp Swarm算法优化的LSTM (SSA-LSTMIDS) 模型在所有测试数据集中实现了卓越的性能.
  • 使用包括准确性,精度,回忆,F-score,真实阳性率 (TPR),假阳性率 (FPR) 和接收器操作特征 (ROC) 曲线在内的指标来评估性能.

结论:

  • 优化的深度学习模型,特别是SSA-LSTM,显著提高了网络入侵检测能力.
  • 拟议的SSA-LSTM模型为减少入侵检测系统中的错误报警提供了可行的和有效的解决方案.
  • 这项研究突出了先进优化算法的潜力,提高了基于深度学习的网络安全解决方案.