HD-I-IoT: Hyperdimensional Computing for Resilient Industrial Internet of Things Analytics
HD-I-IoT: Hyperdimensional Computing for Resilient Industrial Internet of Things Analytics
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DOI：
10.23919/date56975.2023.10137045
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Onat Güngör;Tajana Simunic;Baris Aksanli
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作者：
Onat Güngör;Tajana Simunic;Baris Aksanli
工业物联网（I-IoT）通过持续监控设备和分析收集的数据，实现全自动生产系统。机器学习（ML）方法通常用于此类系统中的数据分析。网络攻击是对物联网的严重威胁，因为它们可以操纵后期合法输入，破坏机器学习预测并导致生产系统中断。超高维（HD）计算是一种受大脑启发的机器学习方法，已被证明足够准确，同时非常健壮、快速和节能。在这项工作中，我们使用基于非线性编码的HD进行针对不同对抗性攻击的智能故障诊断。我们的黑盒对抗性攻击首先训练一个替代模型，并使用该训练模型创建扰动测试实例。然后将这些示例转移到目标模型。分类准确度的变化是通过攻击前后的差异来衡量的。这种变化衡量了学习方法的弹性。我们的实验表明，与最先进的深度学习方法相比，HD带来了更有弹性和轻量级的学习解决方案。与最先进的方法相比，HD的弹性提高了67.5%，同时训练速度提高了25.1倍。
Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring de-vices and analyzing collected data. Machine learning (ML) methods are commonly utilized for data analytics in such systems. Cyberattacks are a grave threat to I-IoT as they can manipu-late legitimate inputs, corrupting ML predictions and causing disruptions in the production systems. Hyperdimensional (HD) computing is a brain-inspired ML method that has been shown to be sufficiently accurate while being extremely robust, fast, and energy-efficient. In this work, we use non-linear encoding-based HD for intelligent fault diagnosis against different adversarial attacks. Our black-box adversarial attacks first train a substitute model and create perturbed test instances using this trained model. These examples are then transferred to the target models. The change in the classification accuracy is measured as the difference before and after the attacks. This change measures the resiliency of a learning method. Our experiments show that HD leads to a more resilient and lightweight learning solution than the state-of-the-art deep learning methods. HD has up to 67.5% higher resiliency compared to the state-of-the-art methods while being up to $25.1\times$ faster to train.