Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System

Critical infrastructures like water treatment facilities and power plants depend on industrial control systems (ICS) for monitoring and control, making them vulnerable to cyber attacks and system malfunctions. Traditional ICS anomaly detection methods lack transparency and interpretability, which make it difficult for practitioners to understand and trust the results. This paper proposes a two-phase dual Copula-based Outlier Detection (COPOD) method that addresses these challenges. The first phase removes unwanted outliers using an empirical cumulative distribution algorithm, and the second phase develops two parallel COPOD models based on the output data of phase 1. The method is based on empirical distribution functions, parameter-free, and provides interpretability by quantifying each feature's contribution to an anomaly. The method is also computationally and memory-efficient, suitable for low- and high-dimensional datasets. Experimental results demonstrate superior performance in terms of F1-score and recall on three open-source ICS datasets, enabling real-time ICS anomaly detection.Comment: 11 pages, 9 figures, journal articl

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

AutoML for Advanced Monitoring in Digital Manufacturing and Industry 4.0

The emergence of Industry 4.0 and the associated rise of sensing technologies in industrial equipment has made the adoption of Machine Learning (ML) solutions crucial in enhancing and making more efficient enterprise production processes. However, the high demand for ML models often clashes with the small number of professionals capable of handling such projects. For this reason, Automatic Machine Learning (AutoML) tools are considered high-value solutions, thanks to their capability to provide a suitable model for the provided data without the need for the intervention by a ML expert. Indeed, AutoML libraries are designed to be used in an easy way also for people with limited or no experience with ML. In this work, three important tasks involved in manufacturing, that are Anomaly Detection, Visual Anomaly Detection, and Remaining Useful Life Estimation, are considered. After analysing the most critical aspects of each task and the state of the art, possible solutions are proposed for the development of specialised AutoML modules. Additionally, given the increasing emphasis on the interpretability of MLmodels, part of the analysis performed aims at identifying explainability tools, which are particularly important for an AutoML library. In fact, they provide useful motivations for the model predictions, increasing also the user confidence in AutoML tools.The emergence of Industry 4.0 and the associated rise of sensing technologies in industrial equipment has made the adoption of Machine Learning (ML) solutions crucial in enhancing and making more efficient enterprise production processes. However, the high demand for ML models often clashes with the small number of professionals capable of handling such projects. For this reason, Automatic Machine Learning (AutoML) tools are considered high-value solutions, thanks to their capability to provide a suitable model for the provided data without the need for the intervention by a ML expert. Indeed, AutoML libraries are designed to be used in an easy way also for people with limited or no experience with ML. In this work, three important tasks involved in manufacturing, that are Anomaly Detection, Visual Anomaly Detection, and Remaining Useful Life Estimation, are considered. After analysing the most critical aspects of each task and the state of the art, possible solutions are proposed for the development of specialised AutoML modules. Additionally, given the increasing emphasis on the interpretability of MLmodels, part of the analysis performed aims at identifying explainability tools, which are particularly important for an AutoML library. In fact, they provide useful motivations for the model predictions, increasing also the user confidence in AutoML tools

Charge-based compact model of gate-all-around floating gate nanowire with variable oxide thickness for flash memory cell

Due to high gate electrostatic control and introduction of punch and plug process technology, the gate-all-around (GAA) transistor is very promising in, and apparently has been utilized for, flash memory applications. However, GAA Floating Gate (GAA-FG) memory cell still requires high programming voltage that may be susceptible to cell-to-cell interference. Scaling down the tunnel oxide can reduce the Program/Erase (P/E) voltage but degrades the data retention capability. By using Technology-Computer-Aided-Design (TCAD) tools, the concept of tunnel barrier engineering using Variable Oxide Thickness (VARIOT) of low-k/high-k stack is utilized in compensating the trade-off between P/E operation and retention characteristics. Four high-k dielectrics (Si3N4, Al2O3, HfO2 and ZrO2) that are commonly used in semiconductor process technology are examined with SiO2 as its low-k dielectric. It is found that by using SiO2/Al2O3 as the tunnel layer, both the P/E and retention characteristics of GAA-FG can be compensated. About 30% improvement in memory window than conventional SiO2 is obtained and only 1% of charge-loss is predicted after 10 years of applying gate stress of -3.6V. Compact model of GAA-FG is initiated by developing a continuous explicit core model of GAA transistor (GAA Nanowire MOSFET (GAANWFET) and Juntionless Nanowire Transitor (JNT)). The validity of the theory and compact model is identified based on sophisticated numerical TCAD simulator for under 10% maximum error of surface potential. It is revealed that with the inclusion of partial-depletion conduction, the accuracy of the core model for GAANWFET is improved by more than 50% in the subthreshold region with doping-geometry ratio can be as high as about 0.86. As for JNT, despite the model being accurate for doping-geometry ratio upto 0.6, it is also independent of fitting parameters that may vary under different terminal biases or doping-geometry cases. The compact model of GAA-FG is completed by incorperating Charge Balance Model (CBM) into GAA transistor core model where good agreement is obtained with TCAD simulation and published experimental work. The CBM gives better accuracy than the conventional capacitive coupling approach under subthreshold region with approximately 10% error of floating gate potential. Therefore, the proposed compact model can be used to assist experimental work in extracting experimental data

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

ADBench: Anomaly Detection Benchmark

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. Our extensive experiments (98,436 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.Comment: NeurIPS 2022. All authors contribute equally and are listed alphabetically. Code available at https://github.com/Minqi824/ADBenc