论文标题
RES-HD：使用超维计算的对抗攻击的弹性智能故障诊断
RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial Attacks Using Hyper-Dimensional Computing
论文作者
论文摘要
工业物联网（I-IOT）通过不断监视设备并分析收集的数据来实现全自动生产系统。机器学习方法通常用于此类系统中的数据分析。网络攻击是对I-iot的严重威胁，因为它们可以操纵合法投入，破坏ML预测并导致生产系统中的中断。超维计算（HDC）是一种受脑启发的机器学习方法，已证明是足够准确的，同时非常健壮，快速且能效。在这项工作中，我们使用HDC对不同的对抗攻击进行智能故障诊断。我们的Black-Box对抗攻击首先训练替代模型，并使用此训练有素的模型创建扰动的测试实例。然后将这些示例转移到目标模型。分类精度的变化被测量为攻击前后的差异。此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，HDC导致更具弹性和轻量级学习解决方案。与最先进的方法相比，HDC的弹性高达67.5％，同时训练的速度快25.1％。
Industrial Internet of Things (I-IoT) enables fully automated production systems by continuously monitoring devices and analyzing collected data. Machine learning methods are commonly utilized for data analytics in such systems. Cyber-attacks are a grave threat to I-IoT as they can manipulate legitimate inputs, corrupting ML predictions and causing disruptions in the production systems. Hyper-dimensional computing (HDC) is a brain-inspired machine learning method that has been shown to be sufficiently accurate while being extremely robust, fast, and energy-efficient. In this work, we use HDC 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 HDC leads to a more resilient and lightweight learning solution than the state-of-the-art deep learning methods. HDC has up to 67.5% higher resiliency compared to the state-of-the-art methods while being up to 25.1% faster to train.
