对通信和计算的毒害攻击任务分类和检测技术
1Institute of Radiocommunications, Poznan University of Technology, 61-131 Poznan, Poland.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究介绍了一种混合k-means和k-nearest neighbors算法,用于通信和计算 (2C) 任务卸载. 它展示了有效的异常检测对毒性攻击,提高2C任务分配安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 机器学习使通信和计算 (2C) 任务从边缘设备卸载到网络服务器.
- 有效的任务卸载依赖于准确的分类算法,这些算法容易受到敌对攻击.
- 毒害攻击可以降低2C任务分配中使用的机器学习模型的性能.
研究的目的:
- 调查中毒攻击对混合k-means和k-nearest neighbors算法的影响,用于2C任务分类.
- 开发和评估异常检测方法,以识别和减轻2C任务卸载场景中的中毒攻击.
- 评估针对特定类型中毒攻击的拟议对策的有效性.
主要方法:
- 采用混合方法,将k-means和k-nearest neighbors结合起来进行任务分类.
- 模拟的类似噪声的干扰和有针对性的数据特征伪造作为中毒攻击.
- 实施了两种异常检测技术:噪音训练和轮得分测试.
- 评估了分类性能和攻击影响在具有不清楚决策边界的特征区域.
主要成果:
- 毒害攻击显著降低了分类准确性,特别是在决策边界附近.
- 提出的异常检测方法成功识别了有毒样本.
- 实施的反制措施有效地减轻了被考虑的中毒攻击的影响.
- 混合k-means和k'-nearest neighbors方法在与异常检测相结合时显示出弹性.
结论:
- 基于机器学习的2C任务卸载易受复杂的中毒攻击的影响.
- 使用噪音训练和轮得分来检测异常对于保护基于ML的2C系统至关重要.
- 拟议的防御机制提高了2C任务分配对抗敌对威胁的稳定性和可靠性.
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