在本地可重新配置的自组织功能地图,用于高影响的恶意任务提交在移动群众传感
Xuankai Chen1, Murat Simsek1, Burak Kantarci1
1School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave,Ottawa, ON K1N 6N5, Canada.
概括
本研究介绍了使用自组织特征地图 (SOFM) 检测移动群众感知 (MCS) 系统中假任务提交的新算法. 这些精细的SOFM模型显著改善了攻击位置的识别,提高了系统安全性.
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:
- 移动人群传感 (MCS) 系统容易受到基于位置的堵塞攻击.
- 这些攻击提交虚假任务,以耗尽设备资源,如电池和处理器.
研究的目的:
- 开发智能策略来建模假任务提交.
- 在MCS系统中识别攻击位置,以进行有效的防御.
主要方法:
- 利用自组织特征地图 (SOFM) 来模拟假任务提交.
- 引入精细的SOFM算法以改善攻击位置识别.
主要成果:
- 拟议的基于SOFM的模型有效地识别了攻击地点.
- 与以前的模型相比,模拟结果显示了高达139.9%的冲击改善.
- 精细的SOFM架构增强了堵塞攻击的建模.
结论:
- 精细的SOFM方法在MCS中提供了更有效的防御,防止基于位置的堵塞攻击.
- 对虚假任务的智能建模对于确保人群感知平台的安全至关重要.
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