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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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Definition and Detection of Hypervulnerabilities using a Framework for Assessing Port Resilience 24

Definition and Detection of Hypervulnerabilities using a Fra...

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24th International Conference on Harbor, Maritime and Multimodal Logistics Modelling and simulation, HMS 2022

作者： Smith, Katherine Diaz, Rafael Shen, Yuzhong Longo, Francesco Virginia Modeling Analysis & Simulation Center Old Dominion University 1030 University Blvd SuffolkVA23435 United States Department of Computational Modeling and Simulation Engineering Old Dominion University 1300 Engineering & Computational Sciences Building NorfolkVA23529 United States Mechanical Department University of Calabria Via Pietro Bucci Arcavacata CS Rende87036 Italy

ISBN: (纸本)9788885741751

Long term plans for maritime ports are identifying investments that will increase their capacity while decreasing their environmental footprint and operating costs. These changes are leading to increases in complexity at a time when leaner practices are driving investments to become more strategic. As such, this work proposes a generalized definition that allows a system or entity to be classified as exceedingly vulnerable by comparing it to other entities. This definition is developed from a set of definitions gathered from disparate fields. From this definition grounded in theory, the initial rules for complex system implementation are developed and demonstrated on both a small conceptual example and a port example. Finally, conclusions and directions for future work are provided. © 2022 The Authors.

关键词： Investments

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Structural constitutive models for soft biological tissues and biomaterials: the role of mechanical interactions

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Mechanics of Soft Materials 2025年第1期7卷 1-39页

作者： Motiwale, Shruti Sacks, Michael S. James T. Willerson Center for Cardiovascular Modeling and Simulation The Oden Institute for Computational Engineering and Sciences Austin USA The Department of Mechanical Engineering The University of Texas at Austin Austin USA The Department of Biomedical Engineering The University of Texas at Austin Austin USA

In this review, we explore the development of the structural constitutive models for soft biological tissues and biomaterials, focusing on the role of mechanical interactions. Soft tissues, such as myocardium, heart valves, skin, and other fibrous biological structures, display complex, non-linear mechanical behaviors driven by their fibrous composition. While traditional constitutive models have been widely adopted to describe the anisotropic and hyperelastic nature of these tissues, they often fall short in capturing the detailed mechanical interactions between fibers, the ground matrix, and other tissue components under diverse loading conditions. To address these gaps, we focus on the development of meso-structural models that account for mechanism-driven fiber-matrix and fiber-fiber interactions. We delve into the kinematics of these interactions, highlighting the roles of fiber rotation, extension, and shear, and how these mechanisms differ across various tissues and biomaterials. Our review draws on experimental evidence from studies on exogenously crosslinked tissues, myocardium and electrospun polycarbonate urethane biomaterials, which reveal the presence of significant mechanical interactions that influence the overall stress response. In addition, we also discuss recent advancements in computational methods for implementing these models, particularly neural network-based approaches that mitigate the computational challenges typically associated with structural models while maintaining high predictive accuracy. Finally, we propose future directions for multiscale modeling to enhance our understanding of fiber network topologies and their mechanical roles in both native tissues and biomaterials.

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Workflow for investigating thermodynamic, structural and energy properties of condensed polymer systems from Molecular Dynamics

arXiv

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arXiv 2022年

作者： Andrews, James P. Blaisten-Barojas, Estela Center for Simulation and Modeling Department of Computational and Data Sciences George Mason University 4400 University Drive FairfaxVA22030 United States

Soft matter materials and polymers are widely used in the controlled delivery of drugs. simulation and modeling provide insight at the atomic scale enabling a level of control unavailable to experiments. We present a workflow protocol for modeling, simulating, and analyzing structural and thermodynamic response properties of poly-lactic-co-glycolic acid (PLGA), a well-studied and FDA approved material. We concatenate a battery of molecular dynamics, computational chemistry, highly parallel scripting, and analysis tools for generating properties of bulk polymers in the condensed phase. We provide the workflow leading to the glass transition temperature, enthalpy, density, isobaric heat capacity, thermal expansion coefficient, isothermal compressibility, bulk modulus, sonic velocity, cohesive energy, and solubility parameters. Calculated properties agree very well with experiments, when available. This methodology has been extended to a variety of polymer types and environments. © 2022, CC BY-NC-ND.

关键词： Molecular dynamics

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Adaptive Statistical Spatial Downscaling of Precipitation Supported by High-Resolution Atmospheric simulation data for Mountainous Areas of Nepal

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Journal of Meteorological Research 2023年第4期37卷 508-520页

作者： Hua YANG Kun YANG Jun QIN Baohong DING Yaozhi JIANG Yingying CHEN Xu ZHOU Yan WANG Shankar SHARMA National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101 University of Chinese Academy of Sciences Beijing 100049 Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijing 100084 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101 Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101

In complex terrain regions, it is very challenging to obtain high accuracy and resolution precipitation data that are required in land hydrological studies. In this study, an adaptive precipitation downscaling method is proposed based on the statistical downscaling model MicroMet. A key input parameter in the MicroMet is the precipitation adjustment factor(PAF) that shows the elevation dependence of precipitation. Its value is estimated conventionally based on station observations and suffers sparse stations in high altitudes. This study proposes to estimate the PAF value and its spatial variability with precipitation data from high-resolution atmospheric simulations and tests the idea in Nepal of South Himalayas, where rainfall stations are relatively dense. The result shows that MicroMet performs the best with the PAF value estimated from the simulation data at the scale of approximately 1.5 degrees. Not only the value at this scale is qualitatively consistent with early knowledge obtained from intensive observations, but also the downscaling performance with this value is better than or comparable to that with the PAF estimated from dense station data. Finally, it is shown that the PAF estimation, although critical, cannot replace the importance of increasing input station density for downscaling.

关键词： precipitation statistical downscaling precipitation adjustment factor adaptive estimation high resolution dynamical downscaling

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Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning

arXiv

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arXiv 2022年

作者： Andrews, James Gkountouna, Olga Blaisten-Barojas, Estela Center for Simulation and Modeling George Mason University United States Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States

Currently machine learning techniques including neural networks are popular tools for materials and chemical scientists with applications that may provide viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. However, efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for forecasting the energetics of a macromolecular polymer-lipid aggregate solvated in ethyl acetate at ambient conditions. The solvated 4-macromolecule aggregate is considered to be a pre-micellar formation of finite life span. data models generated from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on nanoseconds-long time series of the intra-macromolecules potential energy and their interaction energy with the solvent generated from Molecular Dynamics and containing half million points. Our exhaustive analyses convey that the three recurrent neural network architectures investigated generate data models with limited capability of reproducing the energetic fluctuations and yielding short or long term energetics forecasts with underlying distribution of points inconsistent with the input series distributions. We propose an in silico experimental protocol consisting on forming an ensemble of artificial network models trained on an ensemble of series with additional features from time series containing pre-clustered time patterns of the original series. The forecast process improves by predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. However, the distribution of points from the band of forecasts is not optimal. Although the three inspected recurrent neural networks were unable of generating single models that reproduce the actual fluctuations of the inspected molecular system energies in thermal equilibrium at the nano

关键词： Forecasting

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Pre-trained language models in Spanish for health insurance coverage 5

Pre-trained language models in Spanish for health insurance ...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Aracena, Claudio Rodríguez, Nicolás Rocco, Victor Dunstan, Jocelyn Faculty of Physical and Mathematical Sciences University of Chile Chile Millennium Institute Foundational Research on Data Chile Chilean Safety Association Chile Department of Computer Science Catholic University of Chile Chile Institute for Mathematical and Computational Engineering Catholic University of Chile Chile Center for Mathematical Modeling University of Chile Chile

ISBN: (纸本)9781959429883

The field of clinical natural language processing (NLP) can extract useful information from clinical text. Since 2017, the NLP field has shifted towards using pre-trained language models (PLMs), improving performance in several tasks. Most of the research in this field has focused on English text, but there are some available PLMs in Spanish. In this work, we use clinical PLMs to analyze text from admission and medical reports in Spanish for an insurance and health provider to give a probability of no coverage in a labor insurance process. Our results show that fine-tuning a PLM pretrained with the provider's data leads to better results, but this process is time-consuming and computationally expensive. At least for this task, fine-tuning publicly available clinical PLM leads to comparable results to a custom PLM, but in less time and with fewer resources. Analyzing large volumes of insurance requests is burdensome for employers, and models can ease this task by pre-classifying reports that are likely not to have coverage. Our approach of entirely using clinical-related text improves the current models while reinforcing the idea of clinical support systems that simplify human labor but do not replace it. To our knowledge, the clinical corpus collected for this study is the largest one reported for the Spanish language. © 2023 Association for computational Linguistics.

关键词： Health insurance

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Cross-sectional rainfall observation on the central-western Tibetan Plateau in the warm season:System design and preliminary results

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Science China Earth sciences 2023年第5期66卷 1015-1030页

作者： Kun YANG Yingying CHEN Lazhu Changhui ZHAN Xiaoyan LING Xu ZHOU Yaozhi JIANG Xiangnan YAO Hui LU Xiaogang MA Lin OUYANG Weihao PAN Yanghang REN Changkun SHAO Jiaxin TIAN Yan WANG Hua YANG Siyu YUE Ke ZHANG Dingchi ZHAO Long ZHAO Jianhong ZHOU Mijun ZOU Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijing100084China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth System and Resource EnvironmentInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing100101China Research Center for Ecology College of ScienceTibet UniversityLhasa850011China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Science and Natural Resources ResearchChinese Academy of SciencesBeijing100101China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing400715China

The central and western Tibetan Plateau(CWTP)is characterized by harsh environment and strong interactions among the spheres of earth as well as significant changes in climate and water cycles over the past four *** lack of precipitation observations is a bottleneck for the study of land surface processes in this *** the past six years,we have designed and established two observation transects across the south-north and the west-east in this region to obtain hourly rainfall data during the warm season(May-September).The south-north transect extends from Yadong Valley on the southern slope of the Himalayas to Shuanghu County in the hinterland of the plateau,with a total of 31stations;the west-east transect extends from Shiquanhe in the west to Naqu in the central TP,with a total of 22 *** observation dataset has been applied to clarify the spatiotemporal characteristics of precipitation in the CWTP,to evaluate the quality of typical gridded precipitation products,to support the development of regional climate models,and to reveal the processes of summertime lake-air *** observation dataset has been released in the National Tibetan Plateau data center.

关键词： Central and western Tibetan Plateau Rainfall observation transects Observation uncertainty Spatiotemporal characteristics of precipitation Precipitation data evaluation

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Development of a framework to support informed shipbuilding based on supply chain disruptions

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Procedia Computer Science 2022年 200卷 1093-1102页

作者： Katherine Smith Rafael Diaz Yuzhong Shen Virginia Modeling Analysis Simulation Center Old Dominion University 1030 University Blvd Suffolk VA 23435 United States of America Department of Computational Modeling and Simulation Engineering Old Dominion University 1300 Engineering Computational Sciences Building Norfolk VA 23529 United States of America

In addition to stresses induced by the Covid-19 pandemic, supply chains worldwide have been growing more complex while facing a continuous onslaught of disruptions. This paper presents an analysis and extension of a graph based model for modeling and simulating the effects of such disruptions. The graph based model combines a Bayesian network approach for simulating risks with a network dependency analysis approach for simulating the propagation of disruptions through the network over time. The initial analysis provides evidence supporting extension to for using a multi-layered approach allowing for the inclusion of cyclic features in supply chain models. Initial results for individual layers and the aggregate model are presented and discussed. The paper is concluded with a discussion and recommended directions for future work.

关键词： Supply chain Risk disruption analysis modeling simulation Functional dependency analysis Graphical models

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LinkMed: Entity Recognition and Relation Extraction from Clinical Notes in Spanish

LinkMed: Entity Recognition and Relation Extraction from Cli...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Muñoz-Castro, Carlos Carvallo, Andrés Rojas, Matías Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Department of Computer Science Pontificia Universidad Católica de Chile Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Católica de Chile Chile Center for Mathematical Modeling Universidad de Chile Chile Millenium Institute Foundational Research on Data Chile

Relation extraction is an essential component of Natural Language Processing (NLP) and significantly influences information retrieval and structured information extraction. Within clinical notes, the task is needed to establish connections among illnesses, therapies, indications, and other medical concepts. Motivated by the above, in this work, we propose a two-step model approach for entity linking;in the first step, we solve entity recognition, and in the second, a relation classification approach. We evaluated our approach in a Spanish corpus of the TESTLINK challenge in IberLEF2023 (Iberian Languages Evaluation Forum), comprising 81 clinical notes to train and 80 clinical notes to test. Our results show competitive performance with a precision of 0.47, recall of 0.43, and F1-score of 0.45, presenting an effective strategy for relation extraction from clinical notes in Spanish. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Information retrieval

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