Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization

Kilinçer, İlhan Fırat; Ertam, Fatih; Şengür, Abdulkadir; Tan, Ru San; Acharya, U. Rajendra

doi:10.1016/j.bbe.2022.11.005

Cited by 70 publications

(20 citation statements)

References 35 publications

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“…From the prediction results in Table 7, our model outperforms most of the other models on the ICU and ToN_IoT datasets under most of the metrics, but the model proposed by Ilhan et al [29] has a better Recall and F-score on the ECU-IoHT dataset and a better Recall on the ICU dataset. In addition, the DT and KNN models proposed by Zacho et al have a better Precision and Recall.…”

Section: Ton_iot Dataset Resultsmentioning

confidence: 87%

Collabo: A Collaborative Machine Learning Model and its Application to the Security of Heterogeneous Medical Data in an IoT Network

Ekolle¹,

Ochiai²,

Kohno³

2023

Preprint

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Section: Ton_iot Dataset Resultsmentioning

confidence: 87%

Collabo: A Collaborative Machine Learning Model and its Application to the Security of Heterogeneous Medical Data in an IoT Network

Ekolle¹,

Ochiai²,

Kohno³

2023

Preprint

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“…ME2: A related fault detection scheme utilizing a relation network, yet lacking the prototype module, to discern the value added by this component [ 22 ]. ME3: A fault detection scheme employing a multilayer perceptron, representing a standard in neural network applications [ 23 ]. ME4: A scheme that applies signal forecasting methods, offering a perspective on the utility of time-series predictive analysis in fault detection [ 24 ].…”

Section: Case Studymentioning

confidence: 99%

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks

Sun,

Qin,

Tong

et al. 2024

PLoS ONE

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The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

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“…Recursive Feature Elimination (RFE): This method involves iteratively removing the least important features from the dataset until the desired number of features is obtained. The importance of features is determined by their contribution to the performance of a machine-learning algorithm (Lee et al, 2022;Kilincer et al, 2023;Kumari et al, 2023).…”

Section: Related Workmentioning

confidence: 99%

Explaining Xgboost Predictions With Shap Value: A Comprehensive Guide to Interpreting Decision Tree-Based Models

Ergün

2023

New Trends in Computer Sciences

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Understanding the factors that affect Key Performance Indicators (KPIs) and how they affect them is frequently important in sectors where data and data science are crucial. Machine learning is utilized to model and predict pertinent KPIs in order to do this. Interpretability is important, nevertheless, in order to fully comprehend how the model generates its predictions. It enables users to pinpoint which traits have aided the model’s ability to learn and comprehend the data. A practical approach for evaluating the contribution of input attributes to model learning has evolved in the form of SHAP (SHapley Additive exPlanations offer an index for evaluating the influence of each feature on the forecasts made by the model. In this paper, it is demonstrated that the contribution of features to model learning may be precisely estimated when utilizing SHAP values with decision tree-based models, which are frequently used to represent tabular data.

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Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization

Cited by 70 publications

References 35 publications

Collabo: A Collaborative Machine Learning Model and its Application to the Security of Heterogeneous Medical Data in an IoT Network

Collabo: A Collaborative Machine Learning Model and its Application to the Security of Heterogeneous Medical Data in an IoT Network

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks

Explaining Xgboost Predictions With Shap Value: A Comprehensive Guide to Interpreting Decision Tree-Based Models

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