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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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IEEE International Conference on Big Data

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

ISBN: (纸本)9781665480468

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%.

关键词： Military standards Machine learning algorithms Neural networks Machine learning Big Data Data models Internet

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Empowering software Startups with Agile Methods and Practices: A Design Science Approach

SSRN

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SSRN 2022年

作者： Gandomani, Taghi Javdani Nafchi, Mina Ziaei Parizi, Reza M. Department of Computer Science Shahrekord University Shahrekord Iran CHB Science and Technology Park Shahrekord Iran Data Science Research Group Department of Computer Science Shahrekord University Shahrekord Iran Department of Software Engineering and Game Development Kennesaw State University MariettaGA30060 United States

In recent years, the number of software startups is increasing rapidly and employing a proper software development methodology in these startups is a concern. Recently, these startups have been using agile software development methodologies and practices. However, due to the special conditions of these startups and the focus on producing the Minimum Viable Product, it seems that the use of these methodologies and practices is not done correctly. Accordingly, these startups face more severe challenges, making their success more difficult. The literature review shows that few studies have been done regarding the correct methods of using agile methods and practices in software startups. This research has used Design Science Research Methodology (DSRM) to fill this research gap and develop agile adaptation guidelines in software startups. The developed guidelines were validated with the participation of 23 experts from 7 software startup teams through survey questionnaires and open discussion. Eventually, final guidelines containing 13 recommendations were developed in three categories: selection of agile methods and practices, preparation for adaptation, and application and adaptation of agile methods and practices. The evaluation results show the simplicity of understanding these guidelines, their usefulness, and their support for the expected agility of the software development process. © 2022, The Authors. All rights reserved.

关键词： software design

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Quantum Machine Learning for software Supply Chain Attacks: How Far Can We Go?

Quantum Machine Learning for Software Supply Chain Attacks: ...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Mohammad Masum Mohammad Nazim Md Jobair Hossain Faruk Hossain Shahriar Maria Valero Md Abdullah Hafiz Khan Gias Uddin Shabir Barzanjeh Erhan Saglamyurek Akond Rahman Sheikh Iqbal Ahamed Analytics and Data Science Institute Kennesaw State University USA Department of Computer Science Kennesaw State University USA Department of Software Engineering and Game Development Kennesaw State University USA Department Information Technology Kennesaw State University USA Electrical and Software Engineering Institute for Quantum Science and Technology University of Calgary Canada Department of Computer Science Tennessee Tech University USA Department of Computer Science Marquette University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises-quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.

关键词： Computers Machine learning algorithms Supply chains Neural networks Qubit software algorithms Support vector machine classification

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A systematic literature review of blockchain cyber security

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Digital Communications and Networks 2020年第2期6卷 147-156页

作者： Paul J.Taylor Tooska Dargahi Ali Dehghantanha Reza M.Parizi Kim-Kwang Raymond Choo School of Computing Science&EngineeringUniversity of SalfordManchesterUK Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Software Engineering and Game Development Kennesaw State UniversityMariettaGA30060USA Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008,blockchain has(slowly)become one of the most frequently discussed methods for securing data storage and transfer through decentralized,trustless,peer-to-peer *** research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security *** findings show that the Internet of Things(IoT)lends itself well to novel blockchain applications,as do networks and machine visualization,public-key cryptography,web applications,certification schemes and the secure storage of Personally Identifiable Information(PII).This timely systematic review also sheds light on future directions of research,education and practices in the blockchain and cyber security space,such as security of blockchain in IoT,security of blockchain for AI data,and sidechain security.

关键词： Blockchain Smart contracts Cyber security Distributed ledger technology IoT Cryptocurrency Bitcoin

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Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools

Evolution of Quantum Computing: A Systematic Survey on the U...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Paramita Basak Upama Md Jobair Hossain Faruk Mohammad Nazim Mohammad Masum Hossain Shahriar Gias Uddin Shabir Barzanjeh Sheikh Iqbal Ahamed Akond Rahman Department of Computer Science Marquette University USA Department of Software Engineering and Game Development Kennesaw State University USA Department Computer Science Kennesaw State University USA Department of Information Technology Kennesaw State University USA Schulich School of Engineering University of Calgary Canada Department of Physics and Astronomy University of Calgary Canada Department of Computer Science Tennessee Tech university USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum Computer platforms, Circuit Simulator, Open-source Tools- Cirq, TensorFlow Quantum, ProjectQ etc. that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges, and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.

关键词： Quantum computing Systematics Conferences Quantum mechanics Syntactics Supercomputers Sensors

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Ransomware Classification and Detection With Machine Learning Algorithms

arXiv

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arXiv 2022年

作者： Masum, Mohammad Faruk, Md Jobair Hossain Shahriar, Hossain Qian, Kai Lo, Dan Adnan, Muhaiminul Islam School of Data Science Kennesaw State University United States Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department Computer Science Kennesaw State University United States Institute of Natural Sciences United International University Bangladesh

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F-beta, and precision scores. © 2022, CC BY.

关键词： Malware

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Ride-Hailing for Autonomous Vehicles: Hyperledger Fabric-Based Secure and Decentralize Blockchain Platform

arXiv

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arXiv 2022年

作者： Shivers, Ryan Rahman, Mohammad Ashiqur Faruk, Md Jobair Hossain Shahriar, Hossain Cuzzocrea, Alfredo Ryan, Victor Clincy Department of Computer Science Tennessee Tech University United States Department of Electrical and Computer Engineering Florida International University United States Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States iDEA Lab University of Calabria Rende Italy LORIA Nancy France Department of Computer Science Kennesaw State University United States

Ride-hailing and ride-sharing applications have recently gained popularity as a convenient alternative to traditional modes of travel. Current research into autonomous vehicles is accelerating rapidly and will soon become a critical component of a ride-hailing platforms architecture. Implementing an autonomous vehicle ride-hailing platform proves a difficult challenge due to the centralized nature of traditional ride-hailing architectures. In a traditional ride-hailing environment the drivers operate their own personal vehicles so it follows that a fleet of autonomous vehicles would be required for a centralized ride-hailing platform to succeed. Decentralization of the ride-hailing platform would remove a roadblock along the way to an autonomous vehicle ride-hailing platform by allowing owners of autonomous vehicles to add their vehicles to a community-driven fleet when not in use. Blockchain technology is an attractive choice for this decentralized architecture due to its immutability and fault tolerance. This thesis proposes a framework for developing a decentralized ride-hailing architecture that is verifiably secure. This framework is implemented on the Hyperledger Fabric blockchain platform. The evaluation of the implementation is done by applying known security models, utilizing a static analysis tool, and performing a performance analysis under heavy network load. © 2022, CC BY.

关键词： Autonomous vehicles

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Malware Detection and Prevention using Artificial Intelligence Techniques

arXiv

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arXiv 2022年

作者： Faruk, Md Jobair Hossain Shahriar, Hossain Valero, Maria Barsha, Farhat Lamia Sobhan, Shahriar Khan, Md Abdullah Whitman, Michael Cuzzocrea, Alfredo Lo, Dan Rahman, Akond Wu, Fan 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 Tennessee Tech University United States Department Computer Science Kennesaw State University United States Institute for Cyber Workforce Development Kennesaw State University United States iDEA Lab University of Calabria Rende Italy LORIA Nancy France Department of Computer Science Tuskegee University United States

With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholder's, particularly, end user's security, protecting the data from fraudulent efforts is one of the most pressing concerns. A set of malicious programming code, scripts, active content, or intrusive software that is designed to destroy intended computer systems and programs or mobile and web applications is referred to as malware. According to a study, naive users are unable to distinguish between malicious and benign applications. Thus, computer systems and mobile applications should be designed to detect malicious activities towards protecting the stakeholders. A number of algorithms are available to detect malware activities by utilizing novel concepts including Artificial Intelligence, Machine Learning, and Deep Learning. In this study, we emphasize Artificial Intelligence (AI) based techniques for detecting and preventing malware activity. We present a detailed review of current malware detection technologies, their shortcomings, and ways to improve efficiency. Our study shows that adopting futuristic approaches for the development of malware detection applications shall provide significant advantages. The comprehension of this synthesis shall help researchers for further research on malware detection and prevention using AI. © 2022, CC BY.

关键词： Deep learning

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Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools

arXiv

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arXiv 2022年

作者： Upama, Paramita Basak Faruk, Jobair Hossain Nazim, Mohammad Masum, Mohammad Shahriar, Hossain Uddin, Gias Barzanjeh, Shabir Ahamed, Sheikh Iqbal Rahman, Akond Department of Computer Science and Engineering Eastern University Bangladesh Department of Software Engineering and Game Development Kennesaw State University United States Department Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Schulich School of Engineering University of Calgary Canada Department of Physics and Astronomy University of Calgary Canada Department of Mathematics Statistics and Computer Marquette University United States Department of Computer Science Tennessee Tech university United States

Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum Computer platforms, Circuit Simulator, Open-source Tools Cirq, TensorFlow Quantum, ProjectQ that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community. © 2022, CC BY.

关键词： Circuit simulation

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A comparative study on HIPAA technical safeguards assessment of android mHealth applications

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Smart Health 2022年 26卷 100349页

作者： Mia, Md Raihan Shahriar, Hossain Valero, Maria Sakib, Nazmus Saha, Bilash Barek, Md Abdul Faruk, Md Jobair Hossain Goodman, Ben Khan, Rumi Ahmed Ahamed, Sheikh Iqbal Department of Computer Science at Marquette University WI United States Department of Information Technology at Kennesaw State University GA United States Department of Software Engineering and Game Development at Kennesaw State University GA United States 4A Security & Compliance PA United States Dell Medical School at University of Texas Austin TX United States

Protecting personal health records is becoming increasingly important as more people use Mobile Health applications (mHealth apps) to improve their health outcomes. These mHealth apps enable consumers to monitor their health-related problems, store, manage, and share health records, medical conditions, treatment, and medication. With the increase of mHealth apps accessibility and usability, it is crucial to create, receive, maintain or transmit protected health information (PHI) on behalf of a covered entity or another business associate. The Health Insurance Portability and Accountability Act (HIPAA) provides guidelines to the app developers so that the apps must be compliant with required and addressable Technical Safeguards. However, most mobile app developers, including mHealth apps are not aware of HIPAA security and privacy regulations. Therefore, a research opportunity has emerged to develop an analytical framework to assist the developer to maintain a secure and HIPAA-compliant source code and raise awareness among consumers about the privacy and security of sensitive and personal health information. We proposed an Android source code analysis framework that evaluates twelve HIPAA Technical Safeguards to check whether a mHealth application is HIPAA compliant or not. The implemented meta-analysis and data-flow analysis algorithms efficiently identify the risk and safety features of mHealth apps that violate HIPAA regulations. Furthermore, we addressed API level checking for secure data communication mandated by recent CMS guidelines between third-party mobile health apps and EHR systems. Experimentally, a web-based tool has been developed for evaluating the efficacy of analysis techniques and algorithms. We have investigated 200 top popular Medical and Health & Fitness category Android apps collected from Google Play Store. We identified from the comparative analysis of the HIPAA rules assessment results that authorization to access sensitive resources, data

关键词： Android Data flow analysis HIPAA technical safeguards Metadata analysis mHealth Privacy Security Static analysis

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