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1. 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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2. 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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3. Knowledge Graph based Learning Guidance for Cybersecurity Hands-on Labs

   https://doi.org/10.1145/3300115.3309531  

   Deng, Yuli ; Lu, Duo ; Huang, Dijiang ; Chung, Chun-Jen ; Lin, Fanjie ( May 2019 , ACM Global Computing Education Conference (CompEd))

   Hands-on practice is a critical component of cybersecurity education. Most of the existing hands-on exercises or labs materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. With the advantages of big data and natural language processing (NLP) technologies, constructing a large knowledge graph and mining concepts from unstructured text becomes possible, which motivated us to construct a machine learning based lab exercising plan for cybersecurity education. In the research presented by this paper, we have constructed a knowledge graph in the cybersecurity domain using NLP technologies including machine learning based word embedding and hyperlink-based concept mining. We then utilized the knowledge graph during the regular learning process based on the following approaches: 1. We constructed a web-based front-end to visualize the knowledge graph, which allows students to browse and search cybersecurity-related concepts and the corresponding interdependence relations; 2. We created a personalized knowledge graph for each student based on their learning progress and status; 3.We built a personalized lab recommendation system by suggesting more relevant labs based on students’ past learning history to maximize their learning outcomes. To measure the effectiveness of the proposed solution, we have conducted a use case study and collected survey data from a graduate-level cybersecurity class. Our study shows that, by leveraging the knowledge graph for the cybersecurity area study, students tend to benefit more and show more interests in cybersecurity area. 

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4. Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

   https://doi.org/10.1109/FIE56618.2022.9962643  

   Wei-Kocsis, Jin ; Sabounchi, Moein ; Yang, Baijian ; Zhang, Tonglin ( October 2022 , 2022 IEEE Frontiers in Education Conference (FIE))

   This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks. 

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5. Building Knowledge Graphs from Unstructured Texts: Applications and Impact Analyses in Cybersecurity Education

   https://doi.org/10.3390/info13110526  

   Agrawal, Garima ; Deng, Yuli ; Park, Jongchan ; Liu, Huan ; Chen, Ying-Chih ( November 2022 , Information)

   Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined ontology or an existing labeled entity-relation dataset is difficult. Domains such as cybersecurity education can harness knowledge graphs to create a student-focused interactive and learning environment to teach cybersecurity. Learning cybersecurity involves gaining the knowledge of different attack and defense techniques, system setup and solving multi-facet complex real-world challenges that demand adaptive learning strategies and cognitive engagement. However, there are no standard datasets for the cybersecurity education domain. In this research work, we present a bottom-up approach to curate entity-relation pairs and construct knowledge graphs and question-answering models for cybersecurity education. To evaluate the impact of our new learning paradigm, we conducted surveys and interviews with students after each project to find the usefulness of bot and the knowledge graphs. Our results show that students found these tools informative for learning the core concepts and they used knowledge graphs as a visual reference to cross check the progress that helped them complete the project tasks. 

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