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arXiv 2022年

作者： Faruk, Md Jobair Hossain Subramanian, Santhiya Shahriar, Hossain Valero, Maria Li, Xia Tasnim, Masrura Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Institute for Cybersecurity Workforce Development Kennesaw State University United States

software engineering is the process of systematic, disciplined, quantifiable approach that has significant impact on large-scale and complex software development. Scores of well-established software process models have long been adopted in the software development life cycle that pour stakeholders towards the completion of final software products development. Within the boundary of advanced technology, various emerging and futuristic technology is evolving that really need the attention of software engineering community whether the conventional software process techniques are capable to inherit the core fundamental into the futuristic software development. In this paper, we study the impact of existing software engineering processes and models including Agile, and DevOps in Blockchain-Oriented software engineering. We also examine the essentiality of adopting state-of-art concepts and evolving the current software engineering process for blockchain-oriented systems. We discuss the insight of software project management practices in BOS development. The findings of this study indicate that utilizing state-of-art techniques in software processes for futuristic technology would be challenging and promising research is needed extensively towards addressing and improving state-of-the-art software engineering processes and methodology for novel technologies. © 2022, CC BY.

关键词： Blockchain

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Investigating Novel Approaches to Defend software Supply Chain Attacks

Investigating Novel Approaches to Defend Software Supply Cha...

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IEEE International Conference on software Reliability engineering Workshops (ISSRE Wksp)

作者： Md Jobair Hossain Faruk Masrura Tasnim Hossain Shahriar Maria Valero Akond Rahman Fan Wu Department of Software Engineering and Game Development Kennesaw State University USA Institute for Cybersecurity Workforce Development Kennesaw State University USA Departrnent of Information Technology Kennesaw State University USA Department of Computer Science and Software Engineering (CSSE) Auburn University USA Department of Computer Science Tuskegee University USA

ISBN: (数字)9781665476799

ISBN: (纸本)9781665476805

software supply chain attacks occur during the processes of producing software is compromised, resulting in vulnerabilities that target downstream customers. While the number of successful exploits is limited, the impact of these attacks is significant. Despite increased awareness and research into software supply chain attacks, there is limited information available on mitigating or architecting for these risks, and existing information is focused on singular and independent elements of the supply chain. In this paper, we extensively review software supply chain security using software development tools and infrastructure. We investigate the path that attackers find is least resistant followed by adapting and finding the next best way to complete an attack. We also provide a thorough discussion on how common software supply chain attacks can be prevented, preventing malicious hackers from gaining access to an organization's development tools and infrastructure including the development environment. We considered various SSC attacks on stolen code-sign certificates by malicious attackers and prevented unnoticed malware from passing by security scanners. We are aiming to extend our research to contribute to preventing software supply chain attacks by proposing novel techniques and frameworks.

关键词： Resistance Codes Computer hacking Conferences Supply chains Organizations software

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A Novel IoT-based Framework for Non-Invasive Human Hygiene Monitoring using Machine Learning Techniques

arXiv

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arXiv 2022年

作者： Faruk, Md Jobair Hossain Trivedi, Shashank Masum, Mohammad Valero, Maria Shahriar, Hossain Ahamed, Sheikh Iqbal Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Computer Science Marquette University United States

People's personal hygiene habits speak volumes about the condition of taking care of their bodies and health in daily lifestyle. Maintaining good hygiene practices not only reduces the chances of contracting a disease but could also reduce the risk of spreading illness within the community. Given the current pandemic, daily habits such as washing hands or taking regular showers have taken primary importance among people, especially for the elderly population living alone at home or in an assisted living facility. This paper presents a novel and non-invasive framework for monitoring human hygiene using vibration sensors where we adopt Machine Learning techniques. The approach is based on a combination of a geophone sensor, a digitizer, and a cost-efficient computer board in a practical enclosure. Monitoring daily hygiene routines may help healthcare professionals be proactive rather than reactive in identifying and controlling the spread of potential outbreaks within the community. The experimental result indicates that applying a Support Vector Machine (SVM) for binary classification exhibits a promising accuracy of ~95% in the classification of different hygiene habits. Furthermore, both tree-based classifier (Random Forrest and Decision Tree) outperforms other models by achieving the highest accuracy (100%), which means that classifying hygiene events using vibration and non-invasive sensors is possible for monitoring hygiene activity. © 2022, CC BY.

关键词： Support vector machines

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State-based Sandbox Tool for Distributed Malware Detection with Avoid Techniques

State-based Sandbox Tool for Distributed Malware Detection w...

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IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT)

作者： Pavlo Rehida George Markowsky Anatoliy Sachenko Oleg Savenko Department of Computer Engineering and Information Systems Khmelnytskyi National University Khmelnytskyi Ukraine Department of Software Engineering and Game Development Kennesaw State University Marietta USA Department of Informatics Kazimierz Pulaski University of Technology and Humanities Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technologies are described. Various anti-emulation techniques that are used in modern malware considered. Obfuscation as one primary approach to hide malware malicious actions described and discussed. State of emulator is presented, and the advantages of its usage are covered. Distributed model for malware detection is considered. Basic emulator and its current capabilities presented. Prepared files that represent malware are described. Experimental results for developed files that differs with included avoid techniques are presented. Disadvantages of proposed approach is described. Future research and sandbox improvement are described.

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How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis

arXiv

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arXiv 2022年

作者： Ferrari, Alessio Spoletini, Paola Debnath, Sourav Via G. Moruzzi 1 Pisa56126 Italy Department of Software Engineering and Game Development Kennesaw State University 1100 South Marietta Pkwy MariettaGA30060 United States

Requirements are elicited from the customer and other stakeholders through an iterative process of interviews, prototyping, and other interactive sessions. Then, requirements can be further extended, based on the analysis of the features of competing products available on the market. Understanding how this process takes place can help to identify the contribution of the different elicitation phases, thereby allowing requirements analysts to better distribute their resources. In this work, we empirically study in which way requirements get transformed from initial ideas into documented needs, and then evolve based on the inspiration coming from similar products. To this end, we select 30 subjects that act as requirements analysts, and we perform interview-based elicitation sessions with a fictional customer. After the sessions, the analysts produce a first set of requirements for the system. Then, they are required to search similar products in the app stores, and extend the requirements, inspired by the identified apps. The requirements documented at each step are evaluated, to assess to which extent and in which way the initial idea evolved throughout the process. Our results show that only between 30% and 38% of the requirements produced after the interviews include content that can be fully traced to initial customer's ideas. The rest of the content is dedicated to new requirements, and up to 21% of it belongs to completely novel topics. Furthermore, up to 42% of the requirements inspired by the app stores cover additional features compared to the ones identified after the interviews. The results empirically show that requirements are not elicited in strict sense, but actually co-created through interviews, with analysts playing a crucial role in the process. In addition, we show evidence that app store-inspired elicitation can be particularly beneficial to complete the requirements. Copyright © 2022, The Authors. All rights reserved.

关键词： Requirements engineering

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Authentic Learning of Machine Learning to Ransomware Detection and Prevention

Authentic Learning of Machine Learning to Ransomware Detecti...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Md Jobair Hossain Faruk Mohammad Masum Hossain Shahriar Kai Qian Dan Lo Department of Software Engineering and Game Development Kennesaw State University USA School of Data Science Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department Computer Science Kennesaw State University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The primary goal of the authentic learning provides students with an engaging and motivating learning environment for students with hands-on experiences in solving real-world security problems. Each learning topic consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. With an authentic learning approach, we design and develop portable labware on Google CoLab for ML for ransomware detection and prevention so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem-solving skills.

关键词： Conferences Machine learning software Internet Ransomware Computer security

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development of Blockchain-based e-Voting System: Requirements, Design and Security Perspective

Development of Blockchain-based e-Voting System: Requirement...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Md Jobair Hossain Faruk Bilash Saha Mazharul Islam Fazlul Alam Hossain Shahriar Maria Valero Akond Rahman Fan Wu Zakirul Alam Department of Software Engineering and Game Development Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department of Computing Sultan Idris Education University Malaysia Department of Computer Science and Engineering Daffodil International University Bangladesh Department of Computer Science and Software Engineering Auburn University USA Department of Computer Science Tuskegee University USA Department of Computer and Information Sciences Fordham University USA

Elections make a fundamental contribution to democratic governance and are very important to select the appropriate person to determine the fate of a nation while a large number of citizens do not trust the ballot-paper-based voting system. As a result, e-Voting is being adopted by various countries throughout the world. With advantages, there are a number of constraints of e-voting systems, a single vulnerability can lead to large-scale manipulations of voting results. Leveraging and intersecting both cutting-edge technologies including biometric and blockchain technology can address the limitations of current e-Voting frameworks. In this research, we analyze the requirements for systems modeling of voting technology and possible opportunities to adopt biometrics and blockchain technology. Based on the requirements analysis, we propose a biometric-enabled and hyperledger fabric-based voting framework to automate identity verification that will ensure transparency and security of electronic voting. We utilize the software architecture analysis method and active reviews for intermediate designs for evaluating the proposed framework. Demonstration indicates the efficiency and quality attributes of architectural design that shall lead us to implement the system in real-world scenarios in future studies. Initially, we develop a Webbased prototype to demonstrate the framework. In future studies, we aim to implement on large scale and evaluate the prototype against the requirements and security standard.

关键词： System testing Distributed ledger Prototypes Restful API Systems modeling Fabrics Blockchains

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A Review of Quantum Cybersecurity: Threats, Risks and Opportunities

arXiv

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arXiv 2022年

作者： Faruk, Md Jobair Hossain Tahora, Sharaban Tasnim, Masrura Shahriar, Hossain Sakib, Nazmus Department of Software Engineering and Game Development Kennesaw State University United States Department of Computer Science Kennesaw State University United States Institute for Cybersecurity Workforce Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Computer Science and Engineering University at Buffalo The State University of New York United States

The promise of quantum computing is not speeding up conventional computing rather delivering an exponential advantage for certain classes of problems, with profound implications for cybersecurity for instance. With the advent and development of quantum computers, cyberspace security can surely become the most critical problem for the Internet in near future. On contrary, prosaic quantum technology can be promising to transform cybersecurity. This research aims to synthesize basic and fundamental studies concerning quantum cybersecurity that can be emerged both as a threat and solution to critical cybersecurity issues based on a systematic study. We provide a comprehensive, illustrative description of the current state-of-the-art quantum computing and cybersecurity and present the proposed approaches to date. Findings in quantum computing cybersecurity suggest that quantum computing can be adopted for the betterment of cybersecurity threats while it poses the most unexpected threats to cybersecurity. The focus and depth of this systematic survey not only provide quantum and cybersecurity practitioners and researchers with a consolidated body of knowledge about current trends in this area but also underpins a starting point for further research in this field. © 2022, CC BY.

关键词： Cybersecurity

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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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IEEE International Conference on Big Data

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

ISBN: (纸本)9781665480468

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 2 nd 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.

关键词： Bridges Decision making Transportation Machine learning Maintenance engineering Predictive models Big Data

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Systematic Analysis of Deep Learning Model for Vulnerable Code Detection

Systematic Analysis of Deep Learning Model for Vulnerable Co...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Mohammad Taneem Bin Nazim Md Jobair Hossain Faruk Hossain Shahriar Md Abdullah Khan Mohammad Masum Nazmus Sakib Fan Wu Department Computer Science Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department of Information Technology Kennesaw State University USA School of Data Science Kennesaw State University USA Department of Computer Science Tuskegee University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

software vulnerabilities have become a serious problem with the emergence of new applications that contain potentially vulnerable or malicious code that can compromise the system. The growing volume and complexity of software source codes have opened a need for vulnerability detection methods to successfully predict malicious codes before being the prey of cyberattacks. As leveraging humans to check sources codes requires extensive time and resources and preexisting static code analyzers are unable to properly detect vulnerable codes. Thus, artificial intelligence techniques, mainly deep learning models, have gained traction to detect source code vulnerability. A systematic review is carried out to explore and understand the various deep learning methods employed for the task and their efficacy as a prediction model. Additionally, a summary of each process and its characteristics are examined and its implementation on specific data sets and their evaluation will be discussed.

关键词： Deep learning Training Analytical models Codes Systematics Computational modeling Predictive models

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