A Multi-Stage Machine Learning Model for Security Analysis in Industrial Control System

Industrial control systems (ICS) involve many key industries, which once attacked will cause heavy losses. However, traditional passive defense methods of cybersecurity have difficulty effectively dealing with increasingly complex threats; a knowledge graph is a new idea to analyze and process data in cybersecurity analysis. We propose a novel overall framework of data-driven industrial control network security defense, which integrated fragmented multisource threat data with an industrial network layout by a cybersecurity knowledge graph. In order to better correlate data to construct a knowledge graph, we propose a distant supervised relation extraction model ResPCNN-ATT; it is based on a deep residual convolutional neural network and attention mechanism, reduces the influence of noisy data in distant supervision, and better extracts deep semantic features in sentences by using deep residuals. We empirically demonstrate the performance of the proposed method in the field of general cybersecurity by using dataset CSER; the model proposed in this paper achieves higher accuracy than other models. And then, the dataset ICSER was used to construct a cybersecurity knowledge graph (CSKG) on the basis of analyzing specific industrial control scenarios, visualizing the knowledge graph for further security analysis to the industrial control system.

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Designing a Feature Construction and Selection Approach for Machine Learning-Based Intrusion Detection in Industrial Control System Networks

10.5204/thesis.eprints.132657 ◽

2019 ◽

Author(s):

Hassan Fareed M Lahza

Keyword(s):

Machine Learning ◽

Control System ◽

Intrusion Detection ◽

Feature Construction ◽

Industrial Control System ◽

Industrial Control ◽

Selection Approach

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A satellite-based spatio-temporal machine learning model to reconstruct daily PM2.5 concentrations across Great Britain

10.1101/2020.07.19.20157396 ◽

2020 ◽

Author(s):

Rochelle Schneider dos Santos ◽

Ana Vicedo-Cabrera ◽

Francesco Sera ◽

Massimo Stafoggia ◽

Kees de Hoogh ◽

...

Keyword(s):

Machine Learning ◽

Great Britain ◽

Temporal Resolution ◽

Fine Particulate Matter ◽

Epidemiological Studies ◽

Learning Model ◽

Additional Information ◽

Multi Stage ◽

Machine Learning Model ◽

Spatio Temporal

Epidemiological studies on health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolution. The aim of this study is to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain during 2008-2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1-km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. The high spatio-temporal resolution and relatively high precision allows this dataset (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposures to PM2.5.

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