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16th International Conference on Social Computing, Behavioral-Cultural modeling and Prediction and Behavior Representation in modeling and simulation, SBP-BRiMS 2023

作者： Domson, Obed Padilla, Jose J. Song, Guohui Frydenlund, Erika Department of Mathematics and Statistics Old Dominion University NorfolkVA23529 United States Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA23435 United States

ISBN: (纸本)9783031431289

This paper proposes an approximate Bayesian model to predict the number of internally displaced people arriving to a location. Locations are characterized by their elevation, distance from point of departure, and land cover. The model is applied to the population and terrain data of the North Kivu province in the Democratic Republic of Congo (DRC). Results suggest that distance captures about 67% of the influence on the choice of destination;elevation captures 9%, and land cover 24%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Bayesian networks

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An analysis of Global News Coverage of Refugees Using a Big Data Approach 14th

An Analysis of Global News Coverage of Refugees Using a Big ...

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14th International Conference on Social, Cultural, and Behavioral modeling, SBP-BRiMS 2021

作者： Yesilbas, Veysel Padilla, Jose J. Frydenlund, Erika Virginia Modeling Analysis and Simulation Center SuffolkVA United States

ISBN: (纸本)9783030803865

The number of refugees and other displaced persons is at an all-time high globally. News coverage of displacement often evokes negative and even dehumanizing portrayals of the situations, politics, and economics of refugees and their host communities. In this study, we use GDELT to collect a large representation of global news across languages and countries to understand the volume, tone, and topics of news media coverage of refugees. Looking at specific events that trigger peaks in news coverage, we find that while the tone of news is always negative, this negativity reflects both empathy and sorrow for refugees as well as anti-migrant sentiment. World Refugee Day is a significant annual event that drives up news coverage at the international level, even though this also causes an increase in negative tone of the s, reflecting the empathetic aspects of negative tone. At the international level, talking rather than action codes dominate the news. We suggest this as an area of future work, but it points to the large role that international diplomacy and international appeals for aid play in the humanitarian response of refugee situations. © Springer Nature Switzerland AG 2021.

关键词： Big data

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Network Structures and Humanitarian Need 14th

Network Structures and Humanitarian Need

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14th International Conference on Social, Cultural, and Behavioral modeling, SBP-BRiMS 2021

作者： Clark, Mackenzie Frydenlund, Erika Padilla, Jose J. Virginia Modeling Analysis and Simulation Center SuffolkVA United States

ISBN: (纸本)9783030803865

Financial transactions between humanitarian aid donors and recipients form a network that can inform our understanding of how resilient these efforts are collectively. This is particularly critical considering that certain humanitarian "crises" can span years or even decades, leading to donor fatigue in already chronically underfunded situations. We focus on refugee and migrant situations in Greece and Colombia to highlight the disparity between the humanitarian aid networks in responses that have each stretched beyond five years. We observe these financial networks over time to show that they grow through preferential attachment, with large nodes/actors remaining prominent as the years go on. Drawing on the insights from network resilience and foreign aid literature, we highlight the role that private donors play in Greece in providing some robustness to long-term humanitarian responses with no end in sight. Donor diversity appears to play some role in closing the funding gaps, even as need and funding requests for humanitarian response increase over time. © Springer Nature Switzerland AG 2021.

关键词： Finance

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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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U-Net Based Disaster Damage Detection Through Semantic Segmentation

U-Net Based Disaster Damage Detection Through Semantic Segme...

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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Nishat Ara Nipa Yan Lu Sachin Shetty Virginia Modeling Analysis & Simulation Center Old Dominion University Norfolk VA USA

Satellite image analysis of natural disasters is critical for effective emergency response, relief planning, and disaster prevention. They can provide a comprehensive view of the affected area allowing for quick and accurate assessments of the extent of damage by providing critical information on the disaster's development. Semantic segmentation is believed to be one of the best techniques to capture pixel-wise information in computer vision. In this work, we will be using U-Net-based architectures to address the building localization and damage detection of the complex Xview2 dataset. We propose a generic algorithm that is not constrained by the nature of the disaster and can be deployed to various classes of disaster types with slight modifications.

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Towards AI Robustness Multi-Agent Adversarial Planning in Game Play 22

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

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Proceedings of the Winter simulation Conference

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

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.

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Predicting Downlink Retransmissions in 5G Networks Using Deep Learning

Predicting Downlink Retransmissions in 5G Networks Using Dee...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Safdar Hussain Bouk Babatunji Omoniwa Sachin Shetty Virginia Modeling Analysis Simulation Center Old Dominion University Suffolk VA USA Trinity College Dublin Ireland

5G networks are expected to provide high-speed, low-latency, and reliable connectivity to support various applications such as autonomous vehicles, smart cities, and the Internet of Things (IoT). However, the performance of 5G networks can be affected by several factors such as interference, congestion, signal attenuation, or attacks, which can lead to packet loss and retransmissions. Retransmissions in the network may be seen as an essential measure to improve network reliability, but a high retransmission rate may indicate issues that can help network operators mitigate possible service disruptions or threats to network users. A deep learning-based approach has been proposed to predict downlink retransmissions in 5G networks, achieving as much as 5%- 15% improvement over traditional prediction algorithms.

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A quantified hypervulnerability approach for assessing resilience in supply chain networks

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International Journal of simulation and Process Modelling 2024年第3期21卷 166-178页

作者： Smith, Katherine Diaz, Rafael Shen, Yuzhong Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Blvd. SuffolkVA23435 United States School of Cybersecurity Old Dominion University 1113A Monarch Hall NorfolkVA23529 United States Department of Electrical and Computer Engineering Old Dominion University 231 Kaufman Hall NorfolkVA23529 United States

The COVID-19 pandemic highlighted the vulnerability of supply chain networks to sources of concurrent disruption. This research investigates and broadens a quantified, generalised definition of vulnerability, which enables a group of entities to be identified as highly, or hyper, vulnerable. This concept of hypervulnerability thresholds is demonstrated on a conceptual example. The example explains and shows how the definition of hypervulnerability thresholds can be applied. A maritime supply chain case study with various risks and disruptions is presented and analysed to demonstrate the practical utility of the approach and provide valuable information to guide decision making in other applications. Finally, the article concludes with answers to research questions and directions for future work. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： disruption resilience risk supply chain systems theory vulnerability

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Applications of AI/ML in Maritime Cyber Supply Chains

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Procedia Computer Science 2024年 232卷 3247-3257页

作者： Rafael Diaz Ricardo Ungo Katie Smith Lida Haghnegahdar Bikash Singh Tran Phuong Old Dominion University Interdisciplinary Studies School of Cybersecurity Norfolk VA USA Old Dominion University Virginia Modeling Analysis and Simulation Center Norfolk VA USA

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions requiring speed and accuracy. Machine learning and artificial intelligence have become critical in supporting extensive data collection and real-time processing to assist decision-makers in configuring scarce resources. In this paper, we present four different applications that investigate issues related to the broader maritime supply chain security domain affecting the planning, execution, and performance of complex systems while exploring novel frontiers in cyber research and education. This paper will focus on Machine Learning and AI applications on Unmanned Aerial Systems and Cryptography related to Cybersecurity in Maritimes and Shipbuilding Spheres.

关键词： Supply Chain Cybersecurity Maritime and Shipbuilding Artificial Intelligence Machine Learning

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Self-Sovereign Identity Management in Ship-Based 5G-Devices Use Case

Self-Sovereign Identity Management in Ship-Based 5G-Devices ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Peter Foytik Safdar Hussain Bouk Gustave Anderson Sachin Shetty Virginia Modeling Analysis and Simulation Center Old Dominion University Suffolk VA USA Lockheed Martin Roanoke Virginia USA

This paper takes a view on the identity management system in 5G using self-sovereign identity (SSI) solutions. It focuses on the use case of quasi-static and mobile devices that require a good connection on an isolated ship. The identities need to be self-managed when the ship is disconnected at sea, and the newly created identities need to be resolved when the ship regains connection. An overview of the decentralized / distributed identity management in 5G along with the call flows is presented in this paper. Furthermore, we highlight its impact on the large-scale device identity management process and its interoperability with minimum alterations in the current 5G identity management system. This work explores the security benefits of the decentralized property of SSI and measures the performance difference in merging identities with disconnected parts of the system. The results focus on three main measures of 5G identity management systems that include control overhead, verification delay, and identity reconciliation. The paper presents the costs/benefits to the SSI solution compared to centralized identity management systems.

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