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1. Case Study-based Portable Hands-on Labware for Machine Learning in Cybersecurity

   https://doi.org/10.1145/3328778.3372579  

   Shahriar, Hossain ; Whitman, Michael ; Lo, Dan ; Wu, Fan ; Thomas, Cassandra ( February 2020 , Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE))

   Machine Learning (ML) analyzes, and processes data and develop patterns. In the case of cybersecurity, it helps to better analyze previous cyber attacks and develop proactive strategy to detect and prevent the security threats. Both ML and cybersecurity are important subjects in computing curriculum, but ML for cybersecurity is not well presented there. We design and develop case-study based portable labware 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 help students more focus on learning of concepts and getting more experience for hands-on problem solving skills. 

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2. Experiential Learning: Case Study-Based Portable Hands-on Regression Labware for Cyber Fraud Prediction

   https://doi.org/10.1109/BigData47090.2019.9005713  

   Shahriar, Hossain ; Whitman, Miahcel ; Lo, Dan Chia-Tien ; Wu, Fan ; Thomas, Cassandra ; Cuzzocrea, Alfredo ( December 2019 , IEEE International Conference on Big Data (Big Data))

   Machine Learning (ML) analyzes, and processes data and discover patterns. In cybersecurity, it effectively analyzes big data from existing cybersecurity attacks and develop proactive strategies to detect current and future cybersecurity attacks. Both ML and cybersecurity are important subjects in computing curriculum, but using ML for cybersecurity is not commonly explored. This paper designs and presents a case study-based portable labware experience built on Google's CoLaboratory (CoLab) for a ML cybersecurity application to provide students with hands-on labs accessing from anywhere and anytime, reducing or eliminating tedious installations and configurations. This approach allows students to focus on learning essential concepts and gaining valuable experience through hands-on problem solving skills. Our preliminary results and student evaluations are reported for a case-based hands-on regression labware in cyber fraud prediction using credit card fraud as an example. 

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3. Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection

   https://doi.org/10.1109/COMPSAC57700.2023.00161  

   Akter, Mst Shapna ; Shahriar, Hossain ; Iqbal Ahamed, Sheikh ; Datta Gupta, Kishor ; Rahman, Muhammad ; Mohamed, Atef ; Rahman, Mohammad ; Rahman, Akond ; Wu, Fan ( June 2023 , 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC))

   Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin 215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. 

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4. Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection

   Akter, M. ; Shahriar, H. ; Ahamed, S. ; Gupta, K. ; Rahman, M. ; Mohamed, A. ; Rhaman, M. ; Rhaman, A. ( January 2023 , Proc. of IEEE Computers, Software and Applications)

   Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. 

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5. Leveraging a cloud‐based testbed and software‐defined networking for cybersecurity and networking education

   https://doi.org/10.1002/eng2.12395  

   Rahouti, Mohamed ; Xiong, Kaiqi ; Lin, Jing ( July 2021 , Engineering Reports)

   Nowadays, real‐world learning modules become vital components in computer science and engineering in general and cybersecurity in particular. However, as student enrollments have been dramatically increasing, it becomes more challenging for a university/college to keep up with the quality of education that offers hands‐on experiment training for students thoroughly. These challenges include the difficulty of providing sufficient computing resources and keep them upgraded for the increasing number of students. In order for higher education institutions to conquer such challenges, some educators introduce an alternative solution. Namely, they develop and deploy virtual lab experiments on the clouds such as Amazon AWS and the Global Environment for Network Innovations (GENI), where students can remotely access virtual resources for lab experiments. Besides, Software‐Defined Networks (SDN) are an emerging networking technology to enhance the security and performance of networked communications with simple management. In this article, we present our efforts to develop learning modules via an efficient deployment of SDN on GENI for computer networking and security education. Specifically, we first give our design methodology of the proposed learning modules, and then detail the implementations of the learning modules by starting from user account creation on the GENI testbed to advanced experimental GENI‐enabled SDN labs. It is worth pointing out that in order to accommodate students with different backgrounds and knowledge levels, we consider the varying difficulty levels of learning modules in our design. Finally, student assessment over these pedagogical efforts is discussed to demonstrate the efficiency of the proposed learning modules.

    

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