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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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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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Multiclass Classification Approaches for Intrusion Detection in IoT-Driven Aerial Computing Environment

Multiclass Classification Approaches for Intrusion Detection...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Saksham Mittal Amit Kumar Mishra Mohammad Wazid D. P. Singh Ashok Kumar Das Sachin Shetty Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Modeling Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Center for Cybersecurity Education and Research Old Dominion University Suffolk VA USA

Aerial Computing is one of the applications of Internet of Things (IoT) which makes use of autonomous aerial devices, such as drones and unmanned aerial vehicles (UAVs). The ubiquitous nature of IoT and aerial computing is poised to revolutionize our daily lives by enabling seamless real-time information sharing among interconnected objects. However, ensuring the safety and security of such network is crucial in preventing potential threats and attacks. The purpose of this study is to develop a sophisticated intrusion detection system that is effective, efficient, and intelligent using complex machine learning models trained on relevant intrusion detection datasets. In this article, the multiclass classification approaches for intrusion detection in IoT-driven aerial computing environment are presented (in short, MCA-IDAC). In the comparative study, it has been observed that proposed MCA-IDAC performs significantly better than the other existing competing schemes, in terms of important performance parameters.

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Integrated Cellular Framework for modeling Ecosystems: Theory and Applications (vol 94, 213, 2017)

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simulation-TRANSACTIONS OF THE SOCIETY FOR modeling AND simulation INTERNATIONAL 2018年第9期94卷 NP1-NP1页

作者： Kazi, Baha Uddin Wainer, Gabriel Department of Systems and Computer Engineering Center for Visualization Simulation and Modeling Carleton University Canada

Integrated Cellular Framework for modeling Ecosystems: Theory and Applications', by Baha Uddin Kazi and Gabriel Wainer, published Online First on 11 March 2017 in the journal of simulation with the DOI: 10.1177/0037549717706007. In this paper, the funding statement was incorrect and the acknowledgements section was not added in the Online First version. This has since been corrected in the Online First version of the article.

关键词： Cell-DEVS modeling and simulation DEVS geographic information systems ecosystems

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Traditional Vs gesture based UAV control

Traditional Vs gesture based UAV control

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AHFE International Conference on Human Factors in Robots and Unmanned Systems, 2018

作者： Sanders, Brian Vincenzi, Dennis Holley, Sam Shen, Yuzhong Department of Engineering and Technology Embry-Riddle Aeronautical University Worldwide Daytona Beach United States Department of Aeronautics Graduate Studies Embry-Riddle Aeronautical University Worldwide Daytona Beach United States Department of Modeling Simulation and Visualization Engineering Old Dominion University NorfolkVA United States

ISBN: (纸本)9783319943459

The purpose of this investigation was to assess user preferences for controlling an autonomous system. A comparison using a virtual environment (VE) was made between a joystick based, game controller and a gesture-based system using the leap motion controller. Command functions included basic flight maneuvers and switching between the operator and drone view. Comparisons were made between the control approaches using a representative quadcopter drone. The VE was designed to minimize the cognitive loading and focus on the flight control. It is a physics-based flight simulator built in Unity3D. Participants first spend time familiarizing themselves with the basic controls and vehicle response to command inputs. They then engaged in search missions. Data was gathered on time spent performing tasks, and post test interviews were conducted to uncover user preferences. Results indicate that while the gesture-based system has some benefits the joystick control is still preferred. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Virtual reality

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Generative Adversarial Network for Improving Deep Learning Based Malware Classification

Generative Adversarial Network for Improving Deep Learning B...

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simulation Winter Conference

作者： Yan Lu Jiang Li Department of Modeling Simulation and Visualization Engineering Old Dominion University Norfolk VA USA Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

The generative adversarial network (GAN) had been successfully applied in many domains in the past, the GAN network provides a new approach for solving computer vision, object detection and classification problems by learning, mimicking and generating any distribution of data. One of the difficulties in deep learning-based malware detection and classification tasks is lacking of training malware samples. With insufficient training data the classification performance of the deep model could be compromised significantly. To solve this issue, in this paper, we propose a method which uses the Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic malware samples. Our experiment results show that by using the DCGAN generated adversarial synthetic malware samples, the classification accuracy of the classifier - a 18-layer deep residual network is significantly improved by approximately 6%.

关键词： Generative adversarial networks Machine learning Data models Computational modeling Trojan horses Gallium nitride

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An Education of simulation Discussion

An Education of Simulation Discussion

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simulation Winter Conference

作者： Andrew J. Collins Ying Thaviphoke James F. Leathrum David Sturrock Department of Engineering Management and Systems Engineering Old Dominion University Norfolk VA USA Department of Modeling Simulation and Visualization Engineering Old Dominion University Norfolk VA USA Simio LLC Sewickley PA USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

This paper takes three different viewpoints on the problems faced in the development of modeling and simulation (M&S) educational material. Two educational environments - academic and commercial - are discussed. The paper starts with a discussion on the establishment of the undergraduate M&S engineering program at Old Dominion University. The paper then explores the education in a commercial environment, discussing the challenges and possible connections between the two environments. The associated issues which have arisen in the M&S community, especially the accessibility problem, are discussed through the outcomes of a workshop. There is a strong relationship between M&S education and the ease of determining M&S usefulness (i.e., the evidence of success in M&S applications). This paper advocates for detailed case studies to be developed and used within the M&S classrooms. The more case studies (both successful and non-successful), the better the chance to understand different types of complex problems.

关键词： modeling Accreditation Industries Government Computer aided instruction Programming

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Academic/industry educational lab for simulation-based test & evaluation of autonomous vehicles 18

Academic/industry educational lab for simulation-based test ...

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2018 Winter simulation Conference, WSC 2018

作者： Leathrum, James F. Mielke, Roland R. Shen, Yuzhong Johnson, Harry Department of Modeling Simulation and Visualization Engineering Old Dominion University NorfolkVA United States Center for Innovation Lockheed Martin Suffolk VA United States

ISBN: (纸本)9781538665725

An academic/industry alliance can improve the preparedness of students to enter the workforce. When the alliance is centered around a laboratory providing students real world research and development experience, both partners benefit from the intellectual engagement. This paper discusses the benefits of a collaborative laboratory being developed in support of the modeling and simulation engineering program at Old Dominion University. The laboratory focuses on simulation-based test and evaluation (T&E) of autonomous vehicles, an important economic and social problem. Economically, driverless cars and drone delivery systems are the wave of the future. Socially there are issues of public acceptance of these systems as evident by public reaction to the infrequent, but highly public, accidents involving driverless cars. The laboratory allows academia and industry to address these important problems by improving the T&E of these systems while also improving student skills, giving undergraduate and graduate students an opportunity to participate in research. © 2018 IEEE

关键词： Autonomous vehicles

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Exploring Spatial and Temporal Patterns of Large-scale Smartphone-based Dangerous Driving Event Data*

Exploring Spatial and Temporal Patterns of Large-scale Smart...

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International Conference on Intelligent Transportation

作者： Di Yang Kun Xie Kaan Ozbay Hong Yang Department of Civil and Urban Engineering New York University Brooklyn NY USA Department of Civil & Environmental Engineering Old Dominion University (ODU) Norfolk VA USA Department of Modeling Simulation & Visualization Engineering Old Dominion University (ODU) Norfolk VA USA

ISBN: (纸本)9781538670255

Dangerous driving events data are widely used as surrogates to traffic crashes. Large-scale dangerous driving events data collected from smartphones are explored in this study. Clustering analysis is performed on dangerous driving events counted in spatial cells. Spatial and temporal patterns of the cluster distributions are then explored. Both the existence of spatial autocorrelation and the similarity of cluster distributions for different time periods are uncovered.

关键词： Computer crashes Smart phones Acceleration Correlation Safety management Sensors

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Implementation of a 3D interactive mobile app for practicing engineering laboratory experiment 125

Implementation of a 3D interactive mobile app for practicing...

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125th ASEE Annual Conference and Exposition

作者： Ren, Shuo Zhu, Zelin McKenzie, Rick Shen, Yuzhong Old Dominion University Department of Modeling Simulation and Visualization Engineering NorfolkVA United States Old Dominion University ESRI RedlandsCA United States United States

Performing laboratory experiment for off-campus and distance learning students is always a challenging task. Unlike other theory-based courses, engineering laboratory experiment focuses heavily on hands-on tasks. Although many web-based and other virtual reality (VR)-based laboratories widely exist, non-portability, difficult accessibility and high cost of these VR-based devices bring disadvantages to the convenience of learning. One solution is to use mobile devices, which are more portable, easier to access and cheaper in cost, and yet can still be interactive and engaging for students to practice and experience hands-on activities. In this paper, we discuss the benefits of using mobile devices for online education and the development of an Android-based 3D interactive mobile app for an undergraduate mechanical engineering laboratory. This app is capable of not only providing off-campus and distance-learning students an opportunity to practice the experiment on mobile devices, but also helping on-campus students to get familiarized with the experiment procedure prior to their physical laboratory experiment. © American Society for engineering Education, 2018.

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