mlsanityData to 14 Oct 2026

Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures

arXiv 2408.14875 ↗First posted 27 Aug 2024cs.LG cs.AI cs.CRcited by 3

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Abstract (arXiv)

The emergence of deep learning models has revolutionized various industries over the last decade, leading to a surge in connected devices and infrastructures. However, these models can be tricked into making incorrect predictions with high confidence, leading to disastrous failures and security concerns. To this end, we explore the impact of adversarial attacks on multivariate time-series forecasting and investigate methods to counter them. Specifically, we employ untargeted white-box attacks, namely the Fast Gradient Sign Method (FGSM) and the Basic Iterative Method (BIM), to poison the inputs to the training process, effectively misleading the model. We also illustrate the subtle modifications to the inputs after the attack, which makes detecting the attack using the naked eye quite difficult. Having demonstrated the feasibility of these attacks, we develop robust models through adversarial training and model hardening. We are among the first to showcase the transferability of these attacks and defenses by extrapolating our work from the benchmark electricity data to a larger, 10-year real-world data used for predicting the time-to-failure of hard disks. Our experimental results confirm that the attacks and defenses achieve the desired security thresholds, leading to a 72.41% and 94.81% decrease in RMSE for the electricity and hard disk datasets respectively after implementing the adversarial defenses.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 72 references that are in the map. First posted on arXiv 27 Aug 2024.

Builds on

33 of 72 references.

References · 33

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21 more
  1. 2019Enhancing Adversarial Example Transferability with an Intermediate Level Attackcited by 140
  2. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regressioncited by 1
  2. 2026Evasion of IoT Malware Detection via Dummy Code Injectioncited by 0
  3. 2026MorphStrata: Layer-Specific Perturbations for Generating Morphence Students in Time-Series Moving Target Defensecited by 0