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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Sadehvand, Mehrdad Moghim, Neda Ghahfarokhi, Behrouz Shahgholi Shetty, Sachin University of Isfahan Department of Computer Engineering Isfahan Iran Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk United States

ISBN: (纸本)9798350335590

Interference is one of the most critical issues in the cellular Device-to-Device (D2D) networks. The sharing of radio resources in cellular and D2D communications offers spectrum efficiency advantages for the cellular network. However, resource sharing also introduces interference, leading to a reduction in the quality of service experienced by users. In this paper, we propose an adversarial Multi-Armed Bandit learning-based transmission Power Control method called MAB-PC for both cellular and D2D transmitters. The objective of this method is to ensure the minimum service quality for users, considering the minimum spectral efficiency and maximum block error rate. Additionally, to address the conflicting objectives of D2D and cellular communications, MAB-PC is modeled as a Pareto optimization problem for D2D transmitters. MAB-PC is a distributed method that minimizes signaling overhead and relies solely on local Channel State Information (CSI). The effectiveness of the proposed method is evaluated based on reliability, total data rate, spectral efficiency, block error rate, and outage probability, demonstrating superior performance compared to its counterpart. © 2023 IEEE.

关键词： Spectrum efficiency

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BROADENING ACCESS TO simulationS FOR END-USERS VIA LARGE LANGUAGE MODELS: CHALLENGES AND OPPORTUNITIES

BROADENING ACCESS TO SIMULATIONS FOR END-USERS VIA LARGE LAN...

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2024 Winter simulation Conference, WSC 2024

作者： Giabbanelli, Philippe J. Padilla, Jose J. Agrawal, Ameeta Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States Dept. of Computer Science Portland State University PortlandOR United States

ISBN: (纸本)9798331534202

Large Language Models (LLMs) are becoming ubiquitous to create intelligent virtual assistants that assist users in interacting with a system, as exemplified in marketing. Although LLMs have been discussed in modeling & simulation (M&S), the community has focused on generating code or explaining results. We examine the possibility of using LLMs to broaden access to simulations, by enabling non-simulation end-users to ask what-if questions in everyday language. Specifically, we discuss the opportunities and challenges in designing such an end-to-end system, divided into three broad phases. First, assuming the general case in which several simulation models are available, textual queries are mapped to the most relevant model. Second, if a mapping cannot be found, the query can be automatically reformulated and clarifying questions can be generated. Finally, simulation results are produced and contextualized for decision-making. Our vision for such system articulates long-term research opportunities spanning M&S, LLMs, information retrieval, and ethics. © 2024 IEEE.

关键词： Structured Query Language

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Exploring Large Language Models for Analyzing Changes in Web Archive Content: A Retrieval-Augmented Generation Approach

Exploring Large Language Models for Analyzing Changes in Web...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Botello, Jhon G. Frew, Lesley Padilla, Jose J. Weigle, Michele C. Old Dominion University Department of Computer Science NorfolkVA United States Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States

ISBN: (纸本)9798350362480

Websites typically display only their most recent content. However, the dynamic nature of the web leads to frequent updates and deletions. Web archives preserve snapshots of earlier versions for those interested in tracking changes over time. Analyzing these changes often requires a manual process that relies on traditional methods focused on terms or phrase-level differences. This study explores the capability of Large Language Models (LLMs), specifically GPT-4o, through a Retrieval-Augmented Generation (RAG) approach for detecting changes in archived web pages. Using WARC-GPT, a RAG pipeline to interact with Web ARChive (WARC) files, we identify and analyze changes across a small set of U.S. federal environmental web pages that changed between 2016 and 2020. Our findings show that GPT-4o can effectively be used to detect inconsistencies in web archive content, including consideration of the change and the semantic context upon which the changes occurred. Our exploration represents an initial step toward using Artificial Intelligence (AI) for deeper and scalable web change analysis. © 2024 IEEE.

关键词： Semantics

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

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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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DEVS/SOA: A Cross-Platform Framework for Net-centric modeling & simulation in DEVS Unified Process

arXiv

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arXiv 2024年

作者： Mittal, Saurabh Risco-Martín, José L. Zeigler, Bernard P. Arizona Center for Integrative Modeling and Simulation Electrical and Computer Engineering University of Arizona TucsonAZ United States

Discrete EVent Specification (DEVS) environments are known to be implemented over middleware systems such as HLA, RMI, CORBA and others. DEVS exhibits concepts of systems theory and modeling and supports capturing the system behavior from the physical and behavioral perspectives. Further, they are implemented using Object-oriented languages like Java and C++. This research work uses the Java platform to implement DEVS over a Service Oriented Architecture (SOA) framework. Called the DEVS/SOA, the framework supports a development and testing environment known as DEVS Unified Process that is built on a model-continuity-based life cycle methodology. DEVS Unified Process allows DEVS-based modeling and simulation (M&S) over net-centric platforms using DEVS/SOA. This framework also provides the crucial feature of run-time composability of coupled systems using SOA. We describe the architecture and designs of the both the server and the client. The client application communicates with multiple servers hosting DEVS simulation services. These simulation services are developed using the proposed symmetrical services architecture wherein the server can act as both a service provider and a service consumer contrary to the unidirectional client-server paradigm. We also discuss how this Services based architecture provides solutions for cross-platform distributed M&S. We demonstrate DEVS/SOA framework with a scenario of Joint Close Air Support specified in Business Process modeling Notation (BPMN). We also provide a real-world application of Network health monitoring using DEVS/SOA layered architectural framework. © 2024, CC BY-SA.

关键词： Common object request broker architecture (CORBA)

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Blockchain and PUF-based secure key establishment protocol for cross-domain digital twins in industrial Internet of Things architecture

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Journal of Advanced Research 2024年 62卷 155-163页

作者： Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Kharel, Rupak Das, Ashok Kumar Shetty, Sachin Rodrigues, Joel J.P.C. School of Psychology and Computer Science University of Central Lancashire Preston United Kingdom Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Software Engineering The University of Lahore Lahore 54590 Pakistan Department of Computer Science University of Sahiwal Punjab Sahiwal 57000 Pakistan Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Department of Modeling Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center and Center for Cybersecurity Education and Research Old Dominion University Suffolk 23435 VA United States COPELABS Lusófona University Campo Grande 376 Lisbon 1749-024 Portugal

Introduction: The Industrial Internet of Things (IIoT) is a technology that connects devices to collect data and conduct in-depth analysis to provide value-added services to industries. The integration of the physical and digital domains is crucial for unlocking the full potential of the IIoT, and digital twins can facilitate this integration by providing a virtual representation of real-world entities. Objectives: By combining digital twins with the IIoT, industries can simulate, predict, and control physical behaviors, enabling them to achieve broader value and support industry 4.0 and 5.0. Constituents of cooperative IIoT domains tend to interact and collaborate during their complicated operations. Methods: To secure such interaction and collaborations, we introduce a blockchain-based cross-domain authentication protocol for IIoT. The blockchain maintains only each domain's dynamic accumulator, which accumulates crucial materials derived from devices, decreasing the overhead. In addition, we use the on-chain accumulator to effectively validate the unlinkable identities of cross-domain IIoT devices. Results: The implementation of the concept reveals the fact that our protocol is efficient and reliable. This efficiency and reliability of our protocol is also substantiated through comparison with state-of-the-art literature. In contrast to related protocols, our protocol exhibits a minimum 22.67% increase in computation cost efficiency and a 16.35% rise in communication cost efficiency. Conclusion: The developed protocol guarantees data transfer security across the domain and thwarts IoT devices from potential physical attacks. Additionally, in order to protect privacy, anonymity and unlinkability are also guaranteed. © 2024

关键词： Digital twins Industrial Internet of Things (IIoT) Key agreement Mutual authentication Physically Unclonable Functions (PUFs)

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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computer modeling in Engineering & Sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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LESSONS LEARNED FROM THE COVID-19 PANDEMIC: AN ANALYSIS OF REGIONAL PHILIPPINE DATA AND COVID-19 OUTCOMES

LESSONS LEARNED FROM THE COVID-19 PANDEMIC: AN ANALYSIS OF R...

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2023 Philippine Geomatics Symposium, PhilGEOS 2023

作者： Gaano, J.B. Rule, C. Celeste, J. Bongolan, V.P. Rivera, R.L. Caoili, S.E. System Modeling and Simulation Lab Department of Computer Science College of Engineering University of the Philippines – Diliman Quezon City Philippines Center for Informatics University of San Agustin Iloilo Philippines College of Social Work and Community Development UP Diliman Philippines UP College of Medicine Philippines

Variations in indicators which may affect COVID-19 outcomes exist among regions in the Philippines. These variations suggest that the impacts of the pandemic differ not only between countries but also within regions and local areas in a country. This study aims to investigate COVID-19 outcomes (case fatality rate or CFR, incidence rate and hospitalization rate) over time and their correlation with regional healthcare and population factors during different phases of the pandemic across the 17 regions of the Philippines. These outcomes were chosen because they are readily available from government data sources, and are simple to work with. We mark five 100-day time periods of the COVID-19 pandemic in the country. The COVID-19 dataset was based on data maintained by the Department of Health (DOH). Spearman’s rank correlation coefficient analysis was used to examine the link between COVID-19 outcomes region-specific indicators referring to population and access to health care resources. Despite their higher vaccination coverage, regions with greater access to health care resources may still be susceptible to future COVID-19 outbreaks due to their urban and densely populated nature. However, vaccines remain effective in reducing COVID-19 hospitalizations in these regions. Therefore, it is crucial to focus on increasing vaccination coverage in rural, underserved regions and implement targeted outbreak control measures in densely populated areas. © Author(s) 2024.

关键词： COVID-19

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An integrated machine learning approach for identifying emergency rescue messages on social media during natural disasters

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Social Network Analysis and Mining 2025年第1期15卷 1-19页

作者： Khallouli, Wael Li, Jiang Huang, Jingwei Rabadi, Ghaith Kovacic, Samuel Department Engineering Management and Systems Engineering Old Dominion University NorfolkVA United States Department Electrical and Computer Engineering Old Dominion University NorfolkVA United States University of Texas Southwestern Medical Center DallasTX United States School of Modeling Simulation and Training University of Central Florida OrlandoFL United States

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that integrates a fine-tuned BERT to extract low-level statistical features and rule-based Regex filters to extract problem-specific features for emergency tweet identification. The proposed model was evaluated on labeled tweets collected from three hurricane events (Harvey, Ian, and Ida). Experimental results showed that our model performed better than several machine learning and deep learning methods in terms of the Area Under the Precision-Recall Curve (AUC-PR) metric for all events. This study contributed to the crisis informatics literature by introducing a novel deep learning approach for automatically identifying actionable information in social media, which can be adapted for similar natural language processing (NLP) tasks. © The Author(s) 2025.

关键词： Tweets

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Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid

Trustworthy Artificial Intelligence Framework for Proactive ...

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2023 Winter simulation Conference, WSC 2023

作者： Munir, Md. Shirajum Shetty, Sachin Rawat, Danda B. Old Dominion University Virginia Modeling Analysis and Simulation Center 1030 University Blvd SuffolkVA23435 United States Howard University Department of Electrical and Computer Science 2400 Sixth Street NW WashingtonDC20059 United States

ISBN: (纸本)9798350369663

The rapid growth of distributed energy resources (DERs), such as renewable energy sources, generators, consumers, and prosumers in the smart grid infrastructure, poses significant cybersecurity and trust challenges to the grid controller. Consequently, it is crucial to identify adversarial tactics and measure the strength of the attacker's DER. To enable a trustworthy smart grid controller, this work investigates a trustworthy artificial intelligence (AI) mechanism for proactive identification and explanation of the cyber risk caused by the control/status message of DERs. Thus, proposing and developing a trustworthy AI framework to facilitate the deployment of any AI algorithms for detecting potential cyber threats and analyzing root causes based on Shapley value interpretation while dynamically quantifying the risk of an attack based on Ward's minimum variance formula. The experiment with a state-of-the-art dataset establishes the proposed framework as a trustworthy AI by fulfilling the capabilities of reliability, fairness, explainability, transparency, reproducibility, and accountability. © 2023 IEEE.

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