保护智能医疗保健网络物理系统免受黑洞和灰洞攻击,使用区块链支持的基尼指数框架
Mannan Javed1, Noshina Tariq1, Muhammad Ashraf1
1Department of Avionics Engineering, Air University, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了基尼指数和基于区块链的黑洞/灰洞RPL (GBG-RPL) 以检测和减轻智能健康监控系统中的网络威胁. GBG-RPL显著提高了安全性和效率,减少了数据包丢失和能源消耗.
科学领域:
- 网络物理系统安全 网络物理系统安全
- 网络入侵检测 网络入侵检测
- 区块链应用 区块链应用
背景情况:
- 网络物理系统 (CPS) 在医疗保健等关键领域变得越来越重要,需要对威胁提供强大的安全性.
- 黑洞和灰洞攻击威胁到CPS的可用性和完整性,当前的方法往往无法区分恶意行为和合法行为.
研究的目的:
- 引入一种新的技术,基尼指数和基于区块链的黑洞/灰洞RPL (GBG-RPL),用于在智能健康监测CPS中有效地检测和减轻黑洞和灰洞攻击.
- 提高CPS内部监控数据的安全性,完整性和可靠性.
主要方法:
- 利用吉尼指数进行异常检测.
- 利用区块链技术增强安全性和弹性框架.
- 在智能健康监测CPS中实施GBG-RPL技术.
主要成果:
- GBG-RPL 实现了 7.18% 的数据包丢失率的降低,并提高了 11.97% 的剩余能源利用率.
- 攻击检测率提高了10.65%,平均检测时间提高了18.88%.
- 优化了网络管理,减少了21.65%的消息开销,并减少了28.34%的端到端延迟.
结论:
- GBG-RPL为智能健康监控CPS提供了可靠性,效率和安全性的显著改进.
- 吉尼指数和区块链的整合提供了对复杂的网络威胁的强有力的防御.
- 拟议的方法在关键绩效指标上优于BCPS-RPL等现有技术.
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