Multiclass data classification using fault detection-based techniques

Basha, Nour; Sheriff, M. Ziyan; Kravaris, Costas; Nounou, Hazem; Nounou, Mohamed

doi:10.1016/j.compchemeng.2020.106786

Cited by 25 publications

(9 citation statements)

References 23 publications

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“…Finally, the authors of ref. [15] showed that a modification of Principal Component Analysis (PCA) performed well in industrial scenarios. In particular, the authors proposed the usage of Multivariate Generalized Likelihood Ratio (GLR) and Moving Window Interval Aggregation (MWIA) together with Interval PCA (IPCA), which is a non‐interpretable and semi‐supervised approach.…”

Section: Related Workmentioning

confidence: 99%

An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios

Gómez

Maimó

Celdrán

et al. 2023

IET Information Security

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When detecting cyberattacks in Industrial settings, it is not sufficient to determine whether the system is suffering a cyberattack. It is also fundamental to explain why the system is under a cyberattack and which are the assets affected. In this context, the Anomaly Detection based on Machine Learning (ML) and Deep Learning (DL) techniques showed great performance when detecting cyberattacks in industrial scenarios. However, two main limitations hinder using them in a real environment. Firstly, most solutions are trained using a supervised approach, which is impractical in the real industrial world. Secondly, the use of black-box ML and DL techniques makes it impossible to interpret the decision made by the model. This article proposes an interpretable and semi-supervised system to detect cyberattacks in Industrial settings. Besides, our proposal was validated using data collected from the Tennessee Eastman Process. To the best of our knowledge, this system is the only one that offers interpretability together with a semi-supervised approach in an industrial setting. Our system discriminates between causes and effects of anomalies and also achieved the best performance for 11 types of anomalies out of 20 with an overall recall of 0.9577, a precision of 0.9977, and a F1-score of 0.9711.

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Section: Related Workmentioning

confidence: 99%

An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios

Gómez

Maimó

Celdrán

et al. 2023

IET Information Security

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“…There is a considerable amount of research that has been conducted to analyze log data, especially in the areas of intrusion detection, forensics, anomaly detection, and fault diagnosis of IoT and networked information systems [6], [10], [41]. However, most of these studies were primarily based on predefined rule-based engines that utilized complex handcrafted heuristics' and pattern matching signatures for developing workflows, templates, and baselines to detect nonconformity from abnormal pattern deviations [31].…”

Section: Introductionmentioning

confidence: 99%

An Online Parsing Framework for Semistructured Streaming System Logs of Internet of Things Systems

Bhattacharya

Ray

Chugh

et al. 2023

IEEE Open J. Instrum. Meas.

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This article presents a novel log abstraction framework based on neural open information extraction (OpenIE) and dynamic word embedding principles. Though various log parsing frameworks are proposed in the literature, the existing frameworks are modeled on predefined heuristics or autoregressive methodologies that work well in offline scenarios. However, these frameworks are less suitable for dynamic self-adaptive systems, such as the Internet of Things (IoT), where the log outputs have diverse contextual variations and disparate time irregularities. Therefore, it is essential to move away from these traditional approaches and develop a systematic model that can effectively analyze log outputs in real-time and increase the system up-time of IoT networks so that they are almost always available. To address these needs, the proposed framework used OpenIE along with term frequency/inverse document frequency (TF/IDF) vectorization for constructing a set of relational triples (aka triple-sets). Additionally, a dynamic pretrained encoder-decoder architecture is utilized to imbibe the positional and contextualized information in its resultant outputs. The adopted methodology has enabled the proposed framework to extract richer word representations with dynamic contextualization of time-sensitive event logs to enhance further downstream activities, such as failure prediction and prognostic analysis of IoT networks. The proposed framework is evaluated on the system event log traces accumulated from a long range wide-area network (LoRaWAN) IoT gateway to proactively determine the probable causes of its various failure scenarios. Additionally, the study also provided a comparative analysis of its mathematical representations with that of the current state-of-the-art (SOTA) approaches to project the advantages and benefits of the proposed model, particularly from its data analytics standpoint.

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“…The first direction considers a single structure of entire process data, that is, global or local. Principal component analysis (PCA), partial least square (PLS), and independent component analysis (ICA) capture the global structure of the data (Basha et al, 2020). PCA is the widely adopted technique in actual industrial processes due to its concise derivation and easy implementation.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

Shah

Ahmed

2022

Transactions of the Institute of Measurement and Control

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Manifold learning is widely adopted for the fault detection of industrial processes. However, the quality of low-dimensional embedding coordinates can be adversely affected by ill-constructed graph Laplacian. An improved locality preserving projection (ILPP) scheme is proposed. ILPP is built on a geometrically inspired Laplacian, and the Riemannian metric is used to find the suitable bandwidth parameter. The proposed approach combines the advantages of ILPP in preserving manifold data structures and those of support vector data description (SVDD) in handling complex process data distributions. Case studies on helix data, hot strip mill, and Pensim benchmark processes demonstrate the utility and feasibility of the proposed approach. The average fault detection rate for proposed ILPP is 99%, which is higher than locality preserving projection (LPP; 87.8%), local tangent space alignment (LTSA; 74.9%), and principal component analysis (PCA; 90.6%).

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Multiclass data classification using fault detection-based techniques

Cited by 25 publications

References 23 publications

An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios

An interpretable semi‐supervised system for detecting cyberattacks using anomaly detection in industrial scenarios

An Online Parsing Framework for Semistructured Streaming System Logs of Internet of Things Systems

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

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