Light-weight federated learning-based anomaly detection for time-series data in industrial control systems

Hương, Trương Thu; Ta, Bac Phuong; Le, Quang A.; Nguyen, Dan Minh; Le, Cong Thanh; Nguyen, Hieu Trung; Thu, Ha; Nguyen, Hung Tai; Tran, Kim Phuc

doi:10.1016/j.compind.2022.103692

Cited by 74 publications

(25 citation statements)

References 24 publications

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“…Further, the proposed framework used blockchain technology to secure FL algorithms against model poisoning attacks. Troung et al [31] proposed a fast, FL framework for identifying anomalies in industrial control systems which is both memory and computation efficient. The authors reported that the proposed approach outperformed other anomaly detection solutions.…”

Section: Anomaly Detection Using Federated Learningmentioning

confidence: 99%

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

et al. 2023

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The smart grid integrates Information and Communication Technologies (ICT) into the traditional power grid to manage the generation, distribution, and consumption of electrical energy. Despite its many advantages, it faces significant challenges, such as detecting abnormal behaviours in the grid. Identifying anomalous behaviours helps to discover unusual user power consumption, faulty infrastructure, power outages, equipment failures, energy thefts, or cyberattacks. Machine learning (ML)-based techniques on smart meter data has shown remarkable results in anomaly detection. However, traditional ML-based anomaly detection requires smart meters to share local data with a central server, which raises concerns regarding data security and user privacy. Server-based model training faces additional challenges, such as the requirement of centralised computing power, reliable network communication, large bandwidth capacity, and latency issues, all of which affect the real-time anomaly detection performance. Motivated by these concerns, we propose a Federated Learning (FL)-based smart grid anomaly detection scheme where ML models are trained locally in smart meters without sharing data with a central server, thus ensuring user privacy. In the proposed approach, a global model is downloaded from the server to smart meters for ondevice training. After local training, local model parameters are sent to the server to improve the global model. We secure the model parameter updates from adversaries using the SSL/TLS protocol. Using standard datasets, we investigate the anomaly detection performance of federated learning and observe that FL models achieve anomaly detection performance comparable to centralised ML models while ensuring user privacy. Further, our study shows that the proposed FL-based models perform efficiently in terms of memory, CPU usage, bandwidth and power consumption at edge devices and are suitable for implementation in resourceconstrained environments, such as smart meters, for anomaly detection.

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Section: Anomaly Detection Using Federated Learningmentioning

confidence: 99%

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

et al. 2023

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“…There has not been much research towards anomaly detection for major industrial systems such as automatic storage and retrieval systems. However, there are other related works that have adopted deep learning models compared to the traditional machine learning model for anomaly detection [ 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Seok

Kareem

Hur

2023

Sensors

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Industry 5.0, also known as the “smart factory,” is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

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“…More recently, deep learning techniques have been applied to multiple domains, for example, the area of manufacturing [21][22][23]. In the area of smart home consumption prediction, ref.…”

Section: Related Workmentioning

confidence: 99%

Data Aging Matters: Federated Learning-Based Consumption Prediction in Smart Homes via Age-Based Model Weighting

et al. 2023

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Smart homes, powered mostly by Internet of Things (IoT) devices, have become very popular nowadays due to their ability to provide a holistic approach towards effective energy management. This is made feasible via the deployment of multiple sensors, which enables predicting energy consumption via machine learning approaches. In this work, we propose FedTime, a novel federated learning approach for predicting smart home consumption which takes into consideration the age of the time series datasets of each client. The proposed method is based on federated averaging but aggregates local models trained on each smart home device to produce a global prediction model via a novel weighting scheme. Each local model contributes more to the global model when the local data are more recent, or penalized when the data are older upon testing for a specific residence (client). The approach was evaluated on a real-world dataset of smart home energy consumption and compared with other machine learning models. The results demonstrate that the proposed method performs similarly or better than other models in terms of prediction error; FedTime achieved a lower mean absolute error of 0.25 compared to FedAvg. The contributions of this work present a novel federated learning approach that takes into consideration the age of the datasets that belong to the clients, experimenting with a publicly available dataset on grid import consumption prediction, while comparing with centralized and decentralized baselines, without the need for data centralization, which is a privacy concern for many households.

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Light-weight federated learning-based anomaly detection for time-series data in industrial control systems

Cited by 74 publications

References 24 publications

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Data Aging Matters: Federated Learning-Based Consumption Prediction in Smart Homes via Age-Based Model Weighting

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