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arXiv 2023年

作者： Crawford, Talia Duong, Scott Fueston, Richard Lawani, Ayorinde Owoade, Samuel Uzoka, Abel Parizi, Reza M. Yazdinejad, Abbas Department of Software Engineering and Game Development Kennesaw State University MariettaGA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada

Artificial Intelligence (AI) refers to the intelligence demonstrated by machines, and within the realm of AI, Machine Learning (ML) stands as a notable subset. ML employs algorithms that undergo training on data sets, enabling them to carry out specific tasks autonomously. Notably, AI holds immense potential in the field of software engineering, particularly in project management and planning. In this literature survey, we explore the use of AI in software engineering and summarize previous works in this area. We first review eleven different publications related to this subject, then compare the surveyed works. We then comment on the possible challenges present in the utilization of AI in software engineering and suggest possible further research avenues and the ways in which AI could evolve with software engineering in the future. © 2023, CC BY.

关键词： Project management

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A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Masum, Mohammad Anjum, Nafisa Hossain Faruk, Md Jobair Shahriar, Hossain Sakib, Nazmus Valero, Maria Karim, M.A. Rahman, Akond Wu, Fan Cuzzocrea, Alfredo San Jose State University Department of Applied Data Science San Jose United States Kennesaw State University Department of Information Technology United States Kennesaw State University Department of Software Engineering and Game Development United States Kennesaw State University Department of Civil and Environmental Engineering United States Auburn University Department of Computer Science and Software Engineering United States Tuskegee University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9781665480451

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country's social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country's 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score. © 2022 IEEE.

关键词： Forecasting

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A Weighted Federated Averaging Framework to Reduce the Negative Influence from the Dishonest Users 1

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13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020

作者： Zhao, Fengpan Huang, Yan Zhu, Saide Malladi, Venkata Wu, Yubao Department of Computer Science Georgia State University AtlantaGA30303 United States Department of Software Engineering and Game Development Kennesaw State University KennesawGA30144 United States

ISBN: (数字)9783030688516

ISBN: (纸本)9783030688509

Federated learning becomes popular for it can train an excellent performance global model without exposing clients’ privacy. However, most FL applications failed to consider there exists fake local trained models returned from attackers or dishonest users. Not only would the fake parameters be harmful to the convergence of the global model but also be wasting of other users’ computational resources. In this paper, we propose a framework to grade the users’ credit score based on the performances of the returned local models on the testing dataset. We also consider historical data using the exponential moving average to give a relatively higher weight for the most recent testing results. The experiments show that our system can efficiently and effectively find out the fake local models and then speed up the convergence of the global model. © 2021, Springer Nature Switzerland AG.

关键词： Statistical tests

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Blockchain-based Medical Image Sharing and Automated Critical-results Notification: A Novel Framework

Blockchain-based Medical Image Sharing and Automated Critica...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Jiyoun Randolph Md Jobair Hossain Faruk Bilash Saha Hossain Shahriar Maria Valero Liang Zhao Nazmus Sakib Department of Information Technology Kennesaw State University GA USA Department of Software Engineering and Game Development Kennesaw State University GA USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

In teleradiology, medical images are transmitted to offsite radiologist for interpretation and the dictation report is sent back to the original site to aid timely diagnosis and proper patient care. Although teleradiology offers great benefits including time and cost efficiency, after-hour coverages, and staffing shortage management, there are some technical and operational limitations to overcome in reaching its full potential. We analyzed the current teleradiology workflow to identify inefficiencies. Image unavailability and delayed critical result communication stemmed from lack of system integration between teleradiology practice and healthcare institutions are among the most substantial factors causing prolonged turnaround time. In this paper, we propose a blockchain-based medical image sharing and automated critical-results notification platform to address the current limitation. We believe the proposed platform will enhance efficiency in workflow by eliminating the need for intermediaries and will benefit patients by eliminating the need for storing medical images in hard copies. While considerable progress was achieved, further research on governance and HIPAA compliance is required to optimize the adoption of the new application. Towards an idea to a working paradigm, we will implement the prototype during the next phase of our study.

关键词： Computational modeling Prototypes System integration Medical services Radiology Time measurement software

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Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware: Neural Network Algorithms for Network Denial of Service (DOS) Detection

Authentic Learning of Machine Learning in Cybersecurity with...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Faruk, Md Jobair Hossain Shahriar, Hossain Qian, Kai Lo, Dan Whitman, Michael Cuzzocrea, Alfredo Wu, Fan Clincy, Victor Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Institute for Cyber Workforce Development Kennesaw State University United States Department Computer Science Kennesaw State University United States IDEA Lab University of Calabria Rende Italy Loria Nancy France Department of Computer Science Tuskegee University United States

ISBN: (纸本)9781665480451

The primary goal of the authentic learning approach is to engage and motivate students in a learning environment that encourages all students in learning. This approach provides students with hands-on experiences in solving real-world security problems. We designed and developed ten learning modules based on 10 cybersecurity cases with different ML solutions. Each learning module consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. All portable labs are made available on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will engage students in learning concepts and getting more experience for hands-on problem-solving skills. In this paper, we adopt Neural Network Algorithms for Network Denial of Service (DOS) Detection where we apply the KDDCup 1999 datasets contain a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. Our primary goal of this lab is to show whether a link is a malicious or safe connection. Our demonstration shows an achieved accuracy of 99.89%. © 2022 IEEE.

关键词： Students

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Blockchain-based Medical Image Sharing and Automated Critical-results Notification: A Novel Framework

arXiv

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arXiv 2022年

作者： Randolph, Jiyoun Faruk, Md Jobair Hossain Shahriar, Hossain Valero, Maria Zhao, Liang Sakib, Nazmus Saha, Bilash Department of Information Technology Kennesaw State University GA United States Department of Software Engineering and Game Development Kennesaw State University GA United States

关键词： Automation

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Specific Emitter Identification Based on Joint Variational Mode Decomposition

arXiv

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arXiv 2024年

作者： Chen, Xiaofang Xu, Wenbo Wang, Yue Huang, Yan Key Laboratory of Universal Wireless Communication Beijing University of Posts and Telecommunications Beijing China Department of Computer Science Georgia State University AtlantaGA United States Department of Software Engineering and Game Development Kennesaw State University MariettaGA30060 United States

Specific emitter identification (SEI) technology is significant in device administration scenarios, such as self-organized networking and spectrum management, owing to its high security. For nonlinear and non-stationary electromagnetic signals, SEI often employs variational modal decomposition (VMD) to decompose the signal in order to effectively characterize the distinct device fingerprint. However, the trade-off of VMD between the robustness to noise and the ability to preserve signal information has not been investigated in the current literature. Moreover, the existing VMD algorithm does not utilize the stability of the intrinsic distortion of emitters within a certain temporal span, consequently constraining its practical applicability in SEI. In this paper, we propose a joint variational modal decomposition (JVMD) algorithm, which is an improved version of VMD by simultaneously implementing modal decomposition on multi-frame signals. The consistency of multi-frame signals in terms of the central frequencies and the inherent modal functions (IMFs) is exploited, which effectively highlights the distinctive characteristics among emitters and reduces noise. Additionally, the complexity of JVMD is analyzed, which is proven to be more computational-friendly than VMD. Simulations of both modal decomposition and SEI that involve real-world datasets are presented to illustrate that when compared with VMD, the JVMD algorithm improves the accuracy of device classification and the robustness towards noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Variational mode decomposition

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A Review of Dark Web: Trends and Future Directions

A Review of Dark Web: Trends and Future Directions

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Shahriar Sobhan Timothy Williams Md Jobair Hossain Faruk Juan Rodriguez Masrura Tasnim Edwin Mathew Jack Wright Hossain Shahriar Institute for Cybersecurity Workforce Development Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department of Computer Science Kennesaw State University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The dark web is often discussed in taboo by many who are unfamiliar with the subject. However, this paper takes a dive into the skeleton of what constructs the dark web by compiling the research of published essays. The Onion Router (TOR) and other discussed browsers are specialized web browsers that provide anonymity by going through multiple servers and encrypted networks between the host and client, hiding the IP address of both ends. This provides difficulty in terms of controlling or monitoring the dark web, leading to its popularity in criminal underworlds. In this work, we provide an overview of data mining and penetration testing tools that are being widely used to crawl and collect data. We compare the tools to provide strengths and weaknesses of the tools while providing challenges of harnessing massive data from dark web using crawlers and penetration testing tools including machine learning (ML) techniques. Despite the effort to crawl dark web has progressed, there are still rooms to advance existing approaches to combat the ever-changing landscape of the dark web.

关键词： Crawlers Machine learning software Browsers Data mining Servers IP networks

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Security Considerations for Virtual Reality Systems

arXiv

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arXiv 2022年

作者： Viswanathan, Karthik Yazdinejad, Abbas Department of Software Engineering Game Development Kennesaw State University GA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada

There is a growing need for authentication methodology in virtual reality applications. Current systems assume that the immersive experience technology is a collection of peripheral devices connected to a personal computer or mobile device. Hence there is a complete reliance on the computing device with traditional authentication mechanisms to handle the authentication and authorization decisions. Using the virtual reality controllers and headset poses a different set of challenges as it is subject to unauthorized observation, unannounced to the user given the fact that the headset completely covers the field of vision in order to provide an immersive experience. As the need for virtual reality experiences in the commercial world increases, there is a need to provide other alternative mechanisms for secure authentication. In this paper, we analyze a few proposed authentication systems and reached a conclusion that a multidimensional approach to authentication is needed to address the granular nature of authentication and authorization needs of a commercial virtual reality applications in the commercial world. © 2022, CC BY.

关键词： Virtual reality

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Leveraging Healthcare API to transform Interoperability: API Security and Privacy

Leveraging Healthcare API to transform Interoperability: API...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Md Jobair Hossain Faruk Arleen Joy Patinga Lornna Migiro Hossain Shahriar Sweta Sneha Department of Software Engineering and Game Development Kennesaw State University USA Department of Information Systems and Security Kennesaw State University USA Department of Information Technology Kennesaw State University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Interoperability remains one of the biggest challenges facing healthcare organizations today. Despite the advancements made through digital transformation and API that allow increased interoperability, patients still have to contend with a different patient portal for each provider they visit. Several health systems are unable to successfully exchange EHR data. API transfer and consolidate patient information including medical history and treatment records across the disparate health care systems. Mobile apps use API to gather data from various medical wearables and add the data to a patient's health record. However, API exposes application logic and sensitive data information giving patient data a window to the World Wide Web and has thus increasingly become a target for attackers. As the need for tighter API security grows, managing APIs becomes more important than ever. The goal of this paper is to provide an overview and discuss research questions that can aid in understanding and building the knowledge base on API data integration and interoperability.

关键词： Privacy Wearable computers Standards organizations Transforms software Windows Security

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