物联网赋权的智能网络安全框架用于无人机在互联网上的入侵检测
Syeda Nazia Ashraf1, Selvakumar Manickam2, Syed Saood Zia3
1Department of Computer Science, Sindh Madressutal Islam University, Karachi, Pakistan.
Scientific reports
|October 27, 2023
概括
本研究介绍了一种用于无人机网络 (NoD) 的新型网络安全系统,该系统使用机器和深度学习来检测网络攻击. 该系统表现出高精度和稳定性,增强了物联网 (IoT) 无人机操作的安全性.
科学领域:
- 网络安全 网络安全
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 无人机技术 无人机技术
- 机器学习和深度学习
背景情况:
- 基于无人机的物联网系统在农业和智能城市等行业提供先进的导航和数据服务.
- 越来越多地使用支持物联网的无人机引入了由于设计缺陷和缺乏标准的重大网络安全漏洞和风险.
- 对物联网基础设施的恶意利用对无人机网络的完整性和隐私构成威胁.
研究的目的:
- 调查无人机网络 (NoD) 中目前的隐私和安全挑战.
- 提出和评估一个强大的网络安全解决方案,用于在无人机支持的物联网环境中检测和减轻网络攻击.
- 强调对安全无人机网络的关键需求,以防止拦截和入侵.
主要方法:
- 开发一个利用深度学习和机器学习技术的网络安全系统.
- 对已建立的数据集 (如CICIDS2017和KDDCup99) 进行拟议模型性能评估.
- 通过超参数调整优化模型性能,在NoD框架中进行有效的数据分类.
主要成果:
- 拟议的系统实现了高精度 (高达90.16%),准确性 (高达91.36%),回忆 (高达90.11%) 和F测量 (高达90.19%).
- B-LSTM和LSTM模型在NoD中检测网络攻击方面表现出了卓越的效率和稳定性.
- 该系统有效地对数据实例进行分类,提高了整体网络安全效率.
结论:
- 开发的深度学习模型为无人机提供了一个安全和强化的网络,大大降低了拦截和入侵风险.
- 这些发现突出了机器和深度学习在解决无人机支持物联网生态系统中不断变化的网络安全挑战方面的潜力.
- 实施先进的网络安全措施对于各种应用中无人机网络的安全可靠运行至关重要.
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