# Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems

arXiv: https://arxiv.org/abs/2309.15995 · HTML: https://mlsanity.com/paper/2309.15995/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 72 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 23 of 23)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Automated Curriculum Learning for Neural Networks](https://mlsanity.com/paper/1704.03003/) (2017)
- [Competence-based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1903.09848/) (2019)
- [Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks](https://mlsanity.com/paper/1802.03796/) (2018)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Webly Supervised Learning of Convolutional Networks](https://mlsanity.com/paper/1505.01554/) (2015)
- [STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation](https://mlsanity.com/paper/1509.03150/) (2015)
- [Curriculum Learning and Minibatch Bucketing in Neural Machine Translation](https://mlsanity.com/paper/1707.09533/) (2017)
- [Curriculum Learning for Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/1905.05816/) (2019)
- [Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives](https://mlsanity.com/paper/1905.10847/) (2019)
- [Adversarial Feature Matching for Text Generation](https://mlsanity.com/paper/1706.03850/) (2017)
- [Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1908.00262/) (2019)
- [Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks](https://mlsanity.com/paper/1611.06204/) (2016)
- [Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities](https://mlsanity.com/paper/2003.13213/) (2020)
- [Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks](https://mlsanity.com/paper/1806.08110/) (2018)
- [Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning](https://mlsanity.com/paper/1605.03852/) (2016)
- [Concept Drift Detection for Streaming Data](https://mlsanity.com/paper/1504.01044/) (2015)
- [Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location](https://mlsanity.com/paper/2007.01135/) (2020)
- [Curriculum Learning in Deep Neural Networks for Financial Forecasting](https://mlsanity.com/paper/1904.12887/) (2019)

## Built on by (most cited first, 6 of 6)

- [Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels](https://mlsanity.com/paper/2504.19816/) (2025)
- [Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions](https://mlsanity.com/paper/2508.08326/) (2025)
- [Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins](https://mlsanity.com/paper/2509.12982/) (2025)
- [iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning](https://mlsanity.com/paper/2504.04374/) (2025)
- [Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring](https://mlsanity.com/paper/2603.25670/) (2026)
- [AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection](https://mlsanity.com/paper/2608.11679/) (2026)

## Abstract (arXiv)

Anomaly detection is critical to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of attacks and CPS themselves, anomaly detection in CPS is becoming more and more challenging. In our previous work, we proposed a digital twin-based anomaly detection method, called ATTAIN, which takes advantage of both historical and real-time data of CPS. However, such data vary significantly in terms of difficulty. Therefore, similar to human learning processes, deep learning models (e.g., ATTAIN) can benefit from an easy-to-difficult curriculum. To this end, in this paper, we present a novel approach, named digitaL twin-based Anomaly deTecTion wIth Curriculum lEarning (LATTICE), which extends ATTAIN by introducing curriculum learning to optimize its learning paradigm. LATTICE attributes each sample with a difficulty score, before being fed into a training scheduler. The training scheduler samples batches of training data based on these difficulty scores such that learning from easy to difficult data can be performed. To evaluate LATTICE, we use five publicly available datasets collected from five real-world CPS testbeds. We compare LATTICE with ATTAIN and two other state-of-the-art anomaly detectors. Evaluation results show that LATTICE outperforms the three baselines and ATTAIN by 0.906%-2.367% in terms of the F1 score. LATTICE also, on average, reduces the training time of ATTAIN by 4.2% on the five datasets and is on par with the baselines in terms of detection delay time.