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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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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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simulation Winter Conference

作者： Philippe J. Giabbanelli Jose J. Padilla Ameeta Agrawal Virginia Modeling Analysis and Simulation Center Old Dominion University Norfolk VA USA Dept. of Computer Science Portland State University Portland OR USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

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.

关键词： Ethics Codes Large language models Virtual assistants simulation Machine vision Decision making Information retrieval

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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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IEEE International Conference on Big Data

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

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

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.

关键词： Large language models Computational modeling Retrieval augmented generation Semantics Pipelines Web pages Focusing Manuals Big Data Data models

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ACCELERATING HYBRID AGENT-BASED MODELS AND FUZZY COGNITIVE MAPS: HOW TO COMBINE AGENTS WHO THINK ALIKE?

arXiv

引用

arXiv 2024年

作者： Giabbanelli, Philippe J. Beerman, Jack T. Virginia Modeling Analysis and Simulation Center Old Dominion University NorfolkVA United States School of Data Science University of Virginia CharlottesvilleVA United States

While Agent-Based Models can create detailed artificial societies based on individual differences and local context, they can be computationally intensive. Modelers may offset these costs through a parsimonious use of the model, for example by using smaller population sizes (which limits analyses in sub-populations), running fewer what-if scenarios, or accepting more uncertainty by performing fewer simulations. Alternatively, researchers may accelerate simulations via hardware solutions (e.g., GPU parallelism) or approximation approaches that operate a tradeoff between accuracy and compute time. In this paper, we present an approximation that combines agents who ‘think alike’, thus reducing the population size and the compute time. Our innovation relies on representing agent behaviors as networks of rules (Fuzzy Cognitive Maps) and empirically evaluating different measures of distance between these networks. Then, we form groups of think-alike agents via community detection and simplify them to a representative agent. Case studies show that our simplifications remain accuracy. © 2024, CC BY-NC-SA.

关键词： Fuzzy Cognitive Maps

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Performance analysis of Indoor 5G NR Systems

Performance Analysis of Indoor 5G NR Systems

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Bikash Chandra Singh Sachin Shetty Praneet Chivate David Wright Alex Alenberg Peter Woodward Virginia Modeling Analysis and Simulation Center Suffolk VA USA School of Cybersecurity Old Dominion University Suffolk VA USA MEC at the Basement Sterling VA USA

The advent of 5G technology holds transformative potential, reshaping industries with its improved connectivity and communication. In this research paper, we conduct a thorough analysis of the performance and capabilities of an indoor 5G network within a controlled lab environment. Utilizing an Amarisoft Callbox as the 5G core, in conjunction with a Remote Radio Head (RRH) and user equipment (UEs), we evaluate network performance across downlink, and uplink transmissions, considering TCP and UDP protocols between the gNodeB and UE. Our examination encompasses essential metrics such as latency, data rate, and CPU usage, providing valuable insights into the system's suitability for diverse applications.

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Towards AI Robustness Multi-Agent Adversarial Planning in Game Play

Towards AI Robustness Multi-Agent Adversarial Planning in Ga...

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simulation Winter Conference

作者： Yan Lu Sachin Shetty Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA

ISBN: (纸本)9781665476621

This paper applied Monte Carlo Tree Search (MCTS) and its variant algorithm “Upper Confidence bounds applied to Trees” (UCT) to Chinese checker game and visualized it in the Ludii games portal. Since the MCTS approach cannot guarantee a finite game length in our simulation and the winning strategy is inefficient, we developed a Convolution Neural Network (CNN) model formulated on top of the MCTS algorithm to improve the performance of the Chinese checker game. Our experiment and simulations show that by using the weak labels generated by MCTS algorithm, the CNN-based model could learn without supervised signals from human interventions and execute the game strategy in a finite time. PyGame and the Ludii game portal are used in our simulation to visualize the game and show the final game results.

关键词： Visualization Monte Carlo methods Convolution Neural networks Games Robustness Planning

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Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid 23

Trustworthy Artificial Intelligence Framework for Proactive ...

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Proceedings of the Winter simulation Conference

作者： Md. Shirajum Munir Sachin Shetty Danda B. Rawat Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Department of Electrical and Computer Science Howard University Washington D.C. USA

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.

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Enhancing Sentiment analysis with Attention Mechanism and Bayesian-Lipschitz Recurrent Neural Networks

Enhancing Sentiment Analysis with Attention Mechanism and Ba...

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Mahmudul Hasan Sachin Shetty Peter B. Foytik Department of Electrical and Computer Engineering Old Dominion University Norfolk Virginia USA Virginia Modeling Analysis and Simulation Center (VMASC) Old Dominion University Suffolk Virginia USA

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

In medical chatbots and other healthcare domains, government agencies and other essential industries use sentiment analysis for public safety, improving the service quality of products, and other necessary purposes. Sometimes, the data is noisy, ambiguous, and contains mixed sentiments, where different parts of the text may convey different emotions, creating a problem in adequately detecting the sentiment. We proposed a novel recurrent neural network called the Bayesian-Lipschitz RNN with an attention mechanism to address these challenges. The Bayesian approach manages noisy and ambiguous data by representing uncertainty and avoiding overfitting. At the same time, the attention mechanism focuses on the most relevant parts of the text, effectively capturing context and mixed sentiments. Then, the Lipschitz Recurrent Neural Networks (LRNN), based on hidden-to-hidden matrices, are employed and constructed using a symmetric-skew decomposition scheme. The hidden-to-hidden matrices are used to update the hidden state and this hidden state is used to predict the sentiment, mitigate the vanishing gradient and stability issues, and got accuracy with 92.45 %.

关键词： Sentiment analysis Recurrent neural networks Attention mechanisms Symmetric matrices Uncertainty Stability analysis Public security Bayes methods Noise measurement Matrix decomposition

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Segment Anything Model Aided Beam Prediction for the Millimeter Wave Communication 24

Segment Anything Model Aided Beam Prediction for the Millime...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Raha, Avi Deb Adhikary, Apurba Munir, Md. Shirajum Qiao, Yu Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin-si17104 Korea Republic of Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States Department of Artificial Intelligence Kyung Hee University Yongin-si17104 Korea Republic of

ISBN: (纸本)9788995004395

Millimeter wave (mmWave) and terahertz (THz) communication protocols employ extensive antenna arrays to ensure reliable signal reception. However, aligning these narrow beams incurs a significant cost in beam training, which increases proportionally with the number of antennas. While beam prediction methods based on semantics from the RGB images demonstrate initial feasibility, they are still low on accuracy. Motivated by the fact that the recently introduced Segment Anything Model (SAM) can generate very accurate masks, which can be considered semantic information in this domain, this study proposes a beam prediction solution based on the masks of SAM. The SAM can extract more accurate semantic data from visual sources. Then instead of using the RGB images directly, the semantics images have been used to predict the beamforming vector using a lightweight LeNet5 model. The experimental results show that the SAM-based proposed method can perform significantly better than the two state-of-the-art deep learning models. The proposed solution method can achieve near 100% in top-5 beam prediction accuracy in real-world communication scenarios. Copyright 2023 KICS.

关键词： Semantics

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

arXiv

引用

arXiv 2024年

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

关键词： Structured Query Language

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