Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems

Hương, Trương Thu; Bac, Ta Phuong; Ha, Kieu Ngan; Hoàng, Nguyễn Viết; Hoang, Nguyen Xuan; Hung, Nguyen Tai; Tran, Kim Phuc

doi:10.1109/access.2022.3173288

Cited by 34 publications

(11 citation statements)

References 29 publications

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“…An edge-computing-based FL architecture tailored for intelligent applications in Smart Manufacturing within the realm of Big Data. The comprehensive solution has demonstrated superior anomaly detection performance and offers rapid response times by executing anomaly detection near the sources of potential attacks, namely, at the edge [89]. The shortcomings of cyber attack detection in ICS are: It is crucial to study the limitations concerning data size and the number of features to maintain stable operation in edge computing environments.…”

Section: Cyber-attacks Detectionmentioning

confidence: 99%

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Batool,

Xu,

Rehman

et al. 2024

JAI

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Federated Learning (FL) appeared as an encouraging approach for handling decentralized data. Creating a FL system needs both machine learning (ML) knowledge and thinking about how to design system software.Researchers have focused a lot on the ML side of FL, but have not paid enough attention to designing the software architecture. So, in this survey, a set of design patterns is described to tackle the design issues. Design patterns are like reusable solutions for common problems that come up when designing software architecture. This paper focuses on (1) design patterns such as architectures, frameworks, client selection protocols, personalization techniques, and model aggregation techniques that are building blocks of the FL system. It inquires about tradeoffs and working principles accompanying each design aspect, providing insights into their effect on the scalability, performance, or security process; (2) elaborates challenges faced in the design and execution of FL systems such as communication efficiency, statistical/system heterogeneity, or security/privacy concerns. It additionally investigates continuous exploration efforts and distinguishes future examination headings to take out the design challenges and upgrade the adequacy of the frameworks, and (3) depicts some FL applications used in industrial control systems along with their limitations that pave a new research gap for industry professionals. This comprehensive study provides a valuable resource for researchers, practitioners, and system designers interested in understanding the design aspects and challenges associated with FL.

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Section: Cyber-attacks Detectionmentioning

confidence: 99%

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Batool,

Xu,

Rehman

et al. 2024

JAI

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“…Differential privacy methods, for example, have been used with XAI systems to make sure that explanations are produced without accidentally revealing private data [10][11][12]. Furthermore, improvements in federated learning make it possible to train collaborative models across decentralized data sources while improving explainability [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

“…3 displays the number of variables with respect to k and b values. Figure 3 (left) shows the case of MDAV for different values of k, the value fluctuates slightly in the range [14,18]. For k = 6 the number of features reach the peak with 18 features.…”

Section: Cervical Cancer Risk Factorsmentioning

confidence: 99%

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Explainable machine learning models with privacy

Bozorgpanah,

Torra

2024

Prog Artif Intell

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The importance of explainable machine learning models is increasing because users want to understand the reasons behind decisions in data-driven models. Interpretability and explainability emerge from this need to design comprehensible systems. This paper focuses on privacy-preserving explainable machine learning. We study two data masking techniques: maximum distance to average vector (MDAV) and additive noise. The former is for achieving k-anonymity, and the second uses Laplacian noise to avoid record leakage and provide a level of differential privacy. We are interested in the process of developing data-driven models that, at the same time, make explainable decisions and are privacy-preserving. That is, we want to avoid the decision-making process leading to disclosure. To that end, we propose building models from anonymized data. More particularly, data that are k-anonymous or that have been anonymized add an appropriate level of noise to satisfy some differential privacy requirements. In this paper, we study how explainability has been affected by these data protection procedures. We use TreeSHAP as our technique for explainability. The experiments show that we can keep up to a certain degree both accuracy and explainability. So, our results show that some trade-off between privacy and explainability is possible for data protection using k-anonymity and noise addition.

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“…Huong et al [148] focus on improving XAI through the use of federated learning, approaching the existing challenges as a Big Data problem with low-powered edge devices feeding a centralized model called FedEX (Federated learning-based Explainable Anomaly Detection for Industrial Control Systems). This allows geographically distributed IIoT environments to leverage the resource-constrained sensor nodes to collect data, which undergoes local pre-processing on a moderately powerful edge node, with those pre-processed readings then forwarded to a more powerful centralized host for further processing and model training based on data aggregated from all the distributed edge nodes of the highly distributed control system.…”

Section: Using Ai/ml For Anomaly Detectionmentioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges.

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Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems

Cited by 34 publications

References 29 publications

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

Explainable machine learning models with privacy

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

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