Refine your search
Collections
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Chingoriwo, Tinashe
- A Cybersecurity Culture Framework for Grassroots Levels in Zimbabwe
Abstract Views :244 |
PDF Views:1
Authors
Affiliations
1 Zimbabwe Academy of Sciences, Trep Building, University of Zimbabwe Harare, ZW
2 Zimbabwe Open University, Corner House, Samora Machel Avenue/ L.takawira Street Harare
1 Zimbabwe Academy of Sciences, Trep Building, University of Zimbabwe Harare, ZW
2 Zimbabwe Open University, Corner House, Samora Machel Avenue/ L.takawira Street Harare
Source
Oriental Journal of Computer Science and Technology, Vol 14, No 1,2,3 (2021), Pagination: 17-34Abstract
Cybersecurity is a combination of technologies, processes and operations that are designed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access.1 The research was purposed to develop a cybersecurity culture framework which ensures that grassroot users of cyberspace are secured from cyber threats. Literature review showed that in Zimbabwe, no research had attempted to come up with a cybersecurity culture framework for grassroot users of cyberspace.The research was guided by the interpretivist paradigm and employed a qualitative methodology. A descriptive research design was used to answer the research questions and unstructured interviews were done to ascertain the cybersecurity needs and challenges of grassroot users of cyberspace. A cybersecurity culture framework was then crafted based on the research findings. The researchers recommended that Zimbabwe should have a cybersecurity vision and strategy that cascades to the grassroot users of cyberspace. Furthermore, the education curricula should be revised so that it incorporates cybersecurity courses at primary and secondary school level. This will then ensure that ICT adoption is matched with cyber hygiene and responsible use of cyberspace.Keywords
Artificial Intelligence; Big Cloud Computing; Culture Framework; Cybersecurity, Data Analytics, Internet Of Things; Machine LearningReferences
- Kabanda G. Performance of Machine Learning and other Artificial Intelligence paradigms in Cybersecurity. Orient.J. Comp. Sci. and Technol; 13(1).
- Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. Cybersecurity data science: an overview from machine learning perspective, 2020. Journal of Big Data. https://doi.org/10.1186/ s40537-020-00318-5
- Berman, D.S., Buczak, A.L., Chavis, J.S., and Corbett, C.L.Survey of Deep Learning Methods for Cyber Security; 2019. doi:10.3390/info10040122
- Bringas, P.B and Santos, I. Bayesian Networks for Network Intrusion Detection, Bayesian Network, Ahmed Rebai (Ed.), ISBN: 978-953-307-124-4; 2010. InTech, Available from: http://www.intechopen.com/ books/bayesian-network/bayesian-networksfornetwork-intrusion-detection
- Bloice, M. and Holzinger, A. A Tutorial on Machine Learning and Data Science Tools with Python. Graz, Austria, 2018: s.n.
- Gcaza, N., Solms, R. Von, & Vuuren, J. Van. An Ontology for a National Cybersecurity Culture Environment,. In Proceedings of the Ninth International Symposium on Human Aspects of Information Security & Assurance (HAISA 2015) (1-10); 2015.
- Malyuk and Miloslavska. Cybersecurity Culture as an Element of IT Professional Training; 2015
- United Nations. Policy Brief. Tackling the challenges of cybersecurity in Africa; 2014
- Al Hogail M. How is the ministry fostering public-private partnerships (PPPs) with local private developers?, 2015. https://oxfordbusinessgroup.com/interview/ right-home-obg-talks-majed-al-hogailministerhousing.
- International Telecommunication Union. Global Security Report; 2008.
- Sharma R.Study of Latest Emerging Trends on Cybersecurity and its Challenges to Society, 2012. International Journal of Scientific and Engineering Research. Vol 3 Issue 6, June 2012
- Wamala, F. ITU National Cybersecurity Strategy Guide. Chemistry & Geneva, Switzerland;2011
- Symantec .Cybercrime and cybersecurity trends in Africa;2016
- Ernst and Young. Cybersecurity and the Internet of Things;2015
- Concierge. Concierge Security Report. Cybersecurity: Trends from 2017 and Predictions for 2018, 2018
- ACS .Cybersecurity: Opportunities, Threats and Challenges; 2016
- SANS Cybersecurity Threat Landscape Survey; 2017
- McAfee Labs Threats Report;2018
- KPMG .Clarity on Cybersecurity. Driving growth with confidence;2018
- Cox, R. & Wang, G. Predicting the US bank failure: A discriminant analysis. Economic Analysis and Policy, Issue 44.2, pp. 201-211;2014
- Yang, C., Yu, M., Hu, F., Jiang, Y., & Li, Y. Utilizing Cloud Computing to address big geospatial data challenges. Computers, Environment and Urban Systems;2017 https://doi.org/10.1016/j.compenvurbsys. 2016.10.010
- Gercke, M. Cybercrime Understanding Cybercrime, Understanding cybercrime: phenomena, chal lenges and legal response;2012
- Murugan, S., and Rajan, M.S. Detecting Anomaly IDS in Network using Bayesian Network,2014. IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 22780661, p- ISSN: 2278-8727, Volume 16, Issue 1, Ver. III (Jan. 2014), PP 01-07;2014 www.iosrjournals.org
- National Institute Of Standards and Technology. Framework for Improving Critical Infrastructure Cybersecurity Version 1.1;2018
- Alkaraz C. and Zeadally S. Critical Infrastructure Protection: Requirements and Challenges for the 21st Century. Journal of Critical Infrastructure Protection (IJCIP), volume 8, Elsevier Science, pp. 53-66, 01/2015;2015
- Schuessler J.H .General Deterrence Theory: Assessing Information Systems Security Effectiveness In Large Versus Small Businesses,2009.Accessible on: https://digital.library.unt.edu/ark:/67531/metadc9829/m2/1/high_res_d/ dissertation.pdf
- Alanezi, M. A., Kamil, A., & Basri, S. A proposed instrument dimensions for e-government service quality, 2010. International Journal of u-and e-Service, 3(4), 1–18.
- Chukwudi A.E, Udoka E, Charles I. Game Theory Basics and Its Application in Cyber Security; 2017. Advances in Wireless Communications and Networks. Vol. 3, No. 4, 2017, pp. 45-49. doi: 10.11648/j. awcn.20170304.13
- Norwegian Institute of International Affairs, 2018
- Bande .Legislating against Cyber Crime in Southern African Development Community: Balancing International Standards with Country-Specific Specificities, 2018. International Journal of Cyber Criminology Volume 12 Issue 1 January-June 2018
- ITU . Measuring the Information Society 2012;2012
- Schia N.N. The cyber frontier and digital pitfalls in the Global South, 2018. Third World Quarterly, 39(5): 821-837.
- Muller P.L. Cybersecurity Capacity Building in Developing Countries. Opportunities and Challenges, 2015.
- Kortjan, N. & Von Solms, R. A conceptual framework for cybersecurity awareness and education in SA, 2014. South African Computer Journal, 52, 29-41., 2014(52), pp.29–41.
- The Republic of Mauritius Cybercrime Strategy 2017-2019,2017
- https://www.webopedia.com/TERM/C/cryptography.html
- https://www.garykessler.net/library/crypto.html
- https://hitachi-id.com/resource/iam-concepts/authentication.html):
- https://www.techopedia.com/definition/10284/integrity).
- Utilizing Deep Reinforcement Learning and QLearning Algorithms for Improved Ethereum Cybersecurity
Abstract Views :98 |
PDF Views:0
Authors
Affiliations
1 Adjunct Professor of Machine Learning Woxsen School of Business, Woxsen University, Hyderabad, IN
2 Department of Information and Marketing Sciences, Midlands State University Faculty of Business Sciences, IN
3 DPhil (Information Technology) Candidate Faculty of Technology, Zimbabwe Open University, ZW
1 Adjunct Professor of Machine Learning Woxsen School of Business, Woxsen University, Hyderabad, IN
2 Department of Information and Marketing Sciences, Midlands State University Faculty of Business Sciences, IN
3 DPhil (Information Technology) Candidate Faculty of Technology, Zimbabwe Open University, ZW
Source
International Journal of Advanced Networking and Applications, Vol 14, No 6 (2023), Pagination: 5742-5753Abstract
The purpose of the research is to explore and develop Deep Reinforcement Learning and Q-Learning algorithms in order to improve Ethereum cybersecurity in contract vulnerabilities, the smart contract market and research leadership in the area. Deep Reinforcement Learning (Deep RL) is gaining popularity among AI researchers due to its ability to handle complex, dynamic, and particularly high-dimensional cyber protection problems. The benchmark of RL is goal-oriented behavior that increases rewards and decreases penalties or losses, and enhances real-time interaction between an agent and its surroundings. The research paper examines the three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks.The Design Science Research Paradigm as applied in Information Systems research was used in this research, as it is hinged on the idea that information and understanding of a design problem and its solution are attained in the crafting of an artefact. The proposed constructs were in the form of Deep Reinforcement Learning and Q-Learning algorithms designed to improve Ethereum cybersecurity. Smart contracts on the Ethereum blockchain can automatically enforce contracts made between two unknown parties. Blockchain (BC) and artificial intelligence (AI) are used together to strengthen one another's skills and complement one another. Consensus algorithms (CAs) of BC and deep reinforcement learning (DRL) in ETS were thoroughly reviewed. In order to integrate many DCRs and provide grid services, this article suggests an effective incentive-based autonomous DCR control and management framework. This framework simultaneously adjusts the grid's active power with accuracy, optimizes DCR allocations, and increases profits for all prosumers and system operators. The best incentives in a continuous action space to persuade prosumers to reduce their energy consumption were found using a model-free deep deterministic policy gradient-based strategy. Extensive experimental experiments were carried out utilizing real-world data to show the framework's efficacy.Keywords
Reinforcement Learning, DRL, Double Q-Learning, Blockchain, Ethereum Blockchain, Cryptocurrencies, ECC, DNS.References
- Ma, T. (2023). Cybersecurity and Ethereum Security Vulnerabilities Analysis. Highlights in Science, Engineering and Technology, 34, 375-381.
- Jogunola, O. et al., "Consensus Algorithms and Deep Reinforcement Learning in Energy Market: A Review," in IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4211-4227, 15 March15, 2021, doi: 10.1109/JIOT.2020.3032162.
- Ma R., Yi Z., Xiang Y., Shi D., Xu C., and Wu H., "A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning," in IEEE Transactions on Industrial Electronics, vol. 70, no. 1, pp. 430-440, Jan. 2023, doi: 10.1109/TIE.2022.3146631.
- Liu, R., Nageotte, F., Zanne, P., de Mathelin, M., & Dresp-Langley, B. (2021). Deep reinforcement learning for the control of robotic manipulation: a focussed mini-review. Robotics, 10(1), 22.
- Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., ... & Miao, Q. (2022). Deep reinforcement learning: a survey. IEEE Transactions on Neural Networks and Learning Systems.
- Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6), 26-38.
- Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909-4926.
- Gu, S., Holly, E., Lillicrap, T., & Levine, S. (2017, May). Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 3389-3396). IEEE.
- Gu, G. Y., Zhu, J., Zhu, L. M., & Zhu, X. (2017). A survey on dielectric elastomer actuators for soft robots. Bioinspiration & biomimetics, 12(1), 011003.
- Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
- Hong, Z. W., Shann, T. Y., Su, S. Y., Chang, Y. H., Fu, T. J., & Lee, C. Y. (2018). Diversity-driven exploration strategy for deep reinforcement learning. Advances in neural information processing systems, 31.
- Finn, C., Abbeel, P., & Levine, S. (2017, July). Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning (pp. 1126-1135). PMLR.
- Schulman, J., Levine, S., Abbeel, P., Jordan, M., & Moritz, P. (2015, June). Trust region policy optimization. In International conference on machine learning (pp. 1889-1897). PMLR.
- Sarwar, S. S., Ankit, A., & Roy, K. (2019). Incremental learning in deep convolutional neural networks using partial network sharing. IEEE Access, 8, 4615-4628.
- Gürtler, N., Büchler, D., & Martius, G. (2021). Hierarchical reinforcement learning with timed subgoals. Advances in Neural Information Processing Systems, 34, 21732-21743.
- Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning: A survey. arXiv preprint arXiv:2009.07888.
- Li, C., Zheng, P., Yin, Y., Wang, B., & Wang, L. (2023). Deep reinforcement learning in smart manufacturing: A review and prospects. CIRP Journal of Manufacturing Science and Technology, 40, 75-101.
- Pereira, A., & Thomas, C. (2020). Challenges of machine learning applied to safety-critical cyber-physical systems. Machine Learning and Knowledge Extraction, 2(4), 579-602.
- Yang, T., Tang, H., Bai, C., Liu, J., Hao, J., Meng, Z., ... & Wang, Z. (2021). Exploration in deep reinforcement learning: a comprehensive survey. arXiv preprint arXiv:2109.06668.
- Whittlestone, J., Arulkumaran, K., & Crosby, M. (2021). The societal implications of deep reinforcement learning. Journal of Artificial Intelligence Research, 70, 1003-1030.
- Kumar, V., & Webster, M. (2021). Importance Sampling based Exploration in Q Learning. arXiv preprint arXiv:2107.00602.
- Sharma, J., Andersen, P. A., Granmo, O. C., & Goodwin, M. (2020). Deep Q-learning with Q-matrix transfer learning for novel fire evacuation environment. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(12), 7363-7381.
- Hase, H., Azampour, M. F., Tirindelli, M., Paschali, M., Simson, W., Fatemizadeh, E., & Navab, N. (2020, October). Ultrasound-guided robotic navigation with deep reinforcement learning. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5534-5541). IEEE.
- Wang, C., Wang, J., Shen, Y., & Zhang, X. (2019). Autonomous navigation of UAVs in large-scale complex environments: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 68(3), 2124-2136.
- Wang, X., Ke, L., Qiao, Z., & Chai, X. (2020). Largescale traffic signal control using a novel multiagent reinforcement learning. IEEE transactions on cybernetics, 51(1), 174-187.
- Toqeer, A., Alghamdi, T., Nadeem, A., Perwej, Y., & Thabet, M. (2021). Cyber security intelligence and ethereum blockchain technology for e-commerce. International Journal, 9(7).
- Ma, T. (2023). Cybersecurity and Ethereum Security Vulnerabilities Analysis. Highlights in Science, Engineering and Technology, 34, 375–381. https://doi.org/10.54097/hset.v34i.5498.
- Alkhalifah, A., Ng, A., Watters, P. A., & Kayes, A. S. M. (2021). A mechanism to detect and prevent ethereum blockchain smart contract reentrancy attacks. Frontiers in Computer Science, 3, 598780.
- Alkhalifah, A., Ng, A., Chowdhury, M. J. M., Kayes, A. S. M., and Watters, P. A. (2019). “An empirical analysis of blockchain cybersecurity incidents,” in 2019 IEEE Asia-Pacific conference on computer science and data engineering (CSDE), Melbourne, Australia, December 9–11, 2019 (IEEE), 1–8. doi:10.1109/CSDE48274.2019.9162381.
- Alkhalifah, A., Ng, A., Kayes, A. S. M., Chowdhury, J., Alazab, M., and Watters, P. A. (2020). “A taxonomy of blockchain threats and vulnerabilities,” in Blockchain for cybersecurity and privacy: architectures, challenges and applications. (Boca Raton, FL: CRC Press Taylor & Francis), Chap. 1, 1–27.
- Samreen, N. F., and Alalfi, M. H. (2020). “Reentrancy vulnerability identification in Ethereum smart contracts,” The institute of electrical and electronics engineers, Inc.(IEEE) conference proceedings, London, ON, February 18, 2020 (IEEE), 22–29.
- Ramos, S., Lela, Melon, L., Ellul, J., (2022). Exploring Blockchains Cyber Security Techno-Regulatory Gap. An Application to Crypto-Asset Regulation in the EU. Conference Paper, June 2022.
- Amos, Z., (2023) The Cybersecurity Risks of Cryptocurrency; Available on : https://cybersecuritymagazine.com/the-cybersecurity-risks-of-cryptocurrency/
- Yassine M, Alazab M,Shojafar M, Romdhani I (2020). Blockchain for Cybersecurity and Privacy: Architectures, Challenges, and Applications. CRC Press, 2020.
- Roohparvar (2022). The Cybersecurity Risks of Cryptocurrency; Available on:https://www.infoguardsecurity.com/the-cybersecurity-risks-of-cryptocurrency/.
- Samreen, N. F., and Alalfi, M. H. (2020). “Reentrancy vulnerability identification in Ethereum smart contracts,” The institute of electrical and electronics engineers, Inc.(IEEE) conference proceedings, London, ON, February 18, 2020 (IEEE), 22–29.
- Hughes, A., Park, A, Kietzmann, J, Archer-Brown, C. (2019). Beyond Bitcoin: What blockchain and distributed ledger technologies mean for firms. Bus. Horiz. 2019, 62, 273–281
- Madnick, S. (2020). Blockchain isn’t as unbreakable as you think. MIT Sloan Manag. Rev. 61 (2), 65–70. doi:10.2139/ssrn.3542542
- Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Decentralized business review, 21260.
- Wendl, M., Doan, M. H., & Sassen, R. (2023). The environmental impact of cryptocurrencies using proof of work and proof of stake consensus algorithms: A systematic review. Journal of Environmental Management, 326, 116530.
- Arslanian, H. (2022). Ethereum. In The Book of Crypto: The Complete Guide to Understanding Bitcoin, Cryptocurrencies and Digital Assets (pp. 91-98). Cham: Springer International Publishing.
- Bhudia, A., Cartwright, A., Cartwright, E., Hernandez-Castro, J., & Hurley-Smith, D. (2022, August). Extortion of a Staking Pool in a Proof-of-Stake Consensus Mechanism. In 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) (pp. 1-6). IEEE.
- Gundaboina, L., Badotra, S., & Tanwar, S. (2022, March). Reducing resource and energy consumption in cryptocurrency mining by using both proof-of-stake algorithm and renewable energy. In 2022 International Mobile and Embedded Technology Conference (MECON) (pp. 605-610). IEEE.
- Scharfman, J., & Scharfman, J. (2022). Additional topics in blockchain and distributed ledger technology. Cryptocurrency Compliance and Operations: Digital Assets, Blockchain and DeFi, 137-153.
- Arslan, C., Sipahioğlu, S., Şafak, E., Gözütok, M., & Köprülü, T. (2021). Comparative analysis and modern applications of PoW, PoS, PPoS blockchain consensus mechanisms and new distributed ledger technologies. Advances in Science, Technology and Engineering Systems Journal, 6(5), 279-290.
- El-Hajj, M., Fadlallah, A., Chamoun, M., & Serhrouchni, A. (2019). A survey of internet of things (IoT) authentication schemes. Sensors, 19(5), 1141.
- Parham, A., & Breitinger, C. (2022). Non-fungible Tokens: Promise or Peril?. arXiv preprint arXiv:2202.06354.
- Hevner, Alan R.; March, Salvatore T.; Park, Jinsoo; and Ram, Sudha. (2004). Design Science in Information Systems Research, MIS Quarterly, (28: 1).
- Hu, Q., Yan, B., Han, Y., & Yu, J. (2021). An improved delegated proof of stake consensus algorithm. Procedia Computer Science, 187, 341-346.
- A Reinforcement Learning Paradigm for Cybersecurity Education and Training
Abstract Views :152 |
PDF Views:1
Authors
Affiliations
1 Machine Learning Woxsen School of Business, Woxsen University, Hyderabad, IN
2 Department of Information and Masrketing Sciences Midlands State University Faculty of Business Sciences, ZW
3 Faculty of Technology, Zimbabwe Open University, ZW
1 Machine Learning Woxsen School of Business, Woxsen University, Hyderabad, IN
2 Department of Information and Masrketing Sciences Midlands State University Faculty of Business Sciences, ZW
3 Faculty of Technology, Zimbabwe Open University, ZW
Source
Oriental Journal of Computer Science and Technology, Vol 16, No 1 (2023), Pagination: 12-45Abstract
Reinforcement learning (RL) is a type of ML, which involves learning from interactions with the environment to accomplish certain long-term objectives connected to the environmental condition. RL takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The key asynchronous RL algorithms are Asynchronous one-step Q learning, Asynchronous one-step SARSA, Asynchronous n-step Q-learning and Asynchronous Advantage Actor-Critic (A3C). The paper ascertains the Reinforcement Learning (RL) paradigm for cybersecurity education and training. The research was conducted using a largely positivism research philosophy, which focuses on quantitative approaches of determining the RL paradigm for cybersecurity education and training. The research design was an experiment that focused on implementing the RL Q-Learning and A3C algorithms using Python. The Asynchronous Advantage Actor-Critic (A3C) Algorithm is much faster, simpler, and scores higher on Deep Reinforcement Learning task. The research was descriptive, exploratory and explanatory in nature. A survey was conducted on the cybersecurity education and training as exemplified by Zimbabwean commercial banks. The study population encompassed employees and customers from five commercial banks in Zimbabwe, where the sample size was 370. Deep reinforcement learning (DRL) has been used to address a variety of issues in the Internet of Things. DRL heavily utilizes A3C algorithm with some Q-Learning, and this can be used to fight against intrusions into host computers or networks and fake data in IoT devices.Keywords
Artificial Intelligence, Cybersecurity, Deep Learning, DRL Applications, E-Learning, Machine Learning, Reinforcement Learning.References
- Kammann, L. (2018). Digitalisierung im Versicherungsvertrieb: Eine Untersuchung der rechtlichen Grenzen und Möglichkeiten unter besonderer Berücksichtigung der Versicherungsvergleichsportale. VVW GmbH.
- Kabanda, G., (2022), “Face Recognition in Machine Learning: A Framework for Dimensionality Reduction Algorithms”, International Journal of Advanced Networking and Applications (IJANA), Volume: 14, Issue: 02, September-October, 2022, Pages: 5396-5407 (2022), ISSN: 0975-0290, http://www.ijana.in/, https://www.ijana.in/papers/V14I2-11. pdf.
- Saravanan, R., & Sujatha, P. (2018, June). A state of art techniques on machine learning algorithms: a perspective of supervised learning approaches in data classification. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 945-949). IEEE.
- Alzubi, J., Nayyar, A., & Kumar, A. (2018, November). Machine learning from theory to algorithms: an overview. In Journal of physics: conference series (Vol. 1142, No. 1, p. 012012). IOP Publishing.
- Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017.
- Sharma, N., Sharma, R., & Jindal, N. (2021). Machine learning and deep learning applications-a vision. Global Transitions Proceedings, 2(1), 24-28
- Oh, D. Y., & Yun, I. D. (2018). Residual error based anomaly detection using auto-encoder in SMD machine sound. Sensors, 18(5), 1308.
- Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 1906-1955.
- Divya, K. S., Bhargavi, P., & Jyothi, S. (2018). Machine learning algorithms in big data analytics. Int. J. Comput. Sci. Eng, 6(1), 63-70.
- Rochan, M. (2020). Efficient deep learning models for video abstraction.
- Sáray, S., Rössert, C. A., Appukuttan, S., Migliore, R., Vitale, P., Lupascu, C. A., ... & Káli, S. (2021). HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLoS computational biology, 17(1), e1008114.
- Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised classification algorithms in machine learning: A survey and review. In Emerging technology in modelling and graphics (pp. 99-111). Springer, Singapore.
- Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2020). A survey of deep learning and its applications: a new paradigm to machine learning. Archives of Computational Methods in Engineering, 27(4), 1071-1092.
- Srivastava, N., Mansimov, E., & Salakhudinov, R. (2015, June). Unsupervised learning of video representations using lstms. In International conference on machine learning (pp. 843-852). PMLR.
- Lemenkova, P. (2018, November). Hierarchical cluster analysis by R language for pattern recognition in the bathymetric data frame: a Case study of the Mariana Trench, Pacific Ocean. In Virtual Simulation, Prototyping and Industrial Design. Proceedings of 5th International Scientific-Practical Conference (Vol. 2, No. 5, pp. 147-152).
- Nozari, H., & Sadeghi, M. E. (2021). Artificial intelligence and Machine Learning for Real-world problems (A survey). International Journal of Innovation in Engineering, 1(3), 38-47.
- Mooney, S. J., & Pejaver, V. (2018). Big data in public health: terminology, machine learning, and privacy. Annual review of public health, 39, 95.
- Alpaydin, E. (2020). Introduction to machine learning. MIT press.
- Iscen, A., Tolias, G., Avrithis, Y., & Chum, O. (2019). Label propagation for deep semi-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5070-5079).
- Jeong, J., Lee, S., Kim, J., & Kwak, N. (2019). Consistency-based semi-supervised learning for object detection. Advances in neural information processing systems, 32.
- Y. Xin et al., "Machine Learning and Deep Learning Methods for Cybersecurity," in IEEE Access, vol. 6, pp. 35365-35381, 2018, doi: 10.1109/ACCESS.2018.2836950.
- J. Zhang, L. Pan, Q. -L. Han, C. Chen, S. Wen and Y. Xiang, "Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey," in IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 3, pp. 377-391, March 2022, doi: 10.1109/JAS.2021.1004261.
- T. T. Nguyen and V. J. Reddi, "Deep Reinforcement Learning for Cyber Security," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3121870.
- Alghamdi, M. I. (2020). Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security. International Journal of Interactive Mobile Technologies, 14(16).
- Kivunja, ,. C. & Kuyini, B. A., 2017. Understanding and Applying Research Paradigms in Educational Contexts. International Journal of Higher Education, 6(26).
- Mohajan, 2017. Qualitative Research Methodology in Social Sciences and Related Subjects. Journal of Economic Development, Environment and People, Volume 7, pp. 23-48.
- Siedlecki, S.L. (2020), “Understanding Descriptive Research Designs and Methods”, Clinical Nurse Specialist, Lippincott Williams and Wilkins, Vol. 34 No. 1, pp. 8–12.
- Kumar, M. (2022), “Classification of Research Design: Descriptive, Diagnostic, Exploratory and Experimental”.
- Casula, M., Rangarajan, N. and Shields, P. (2021), “The potential of working hypotheses for deductive exploratory research”, Quality and Quantity, Springer Science and Business Media B.V., Vol. 55 No. 5, pp. 1703–1725.
- Toyon, M.A.S. (2021), “Explanatory sequential design of mixed methods research: Phases and challenges”, International Journal of Research in Business and Social Science (2147- 4478), Center for Strategic Studies in Business and Finance SSBFNET, Vol. 10 No. 5, pp. 253–260.
- Dileep, P. K., Tröger, J. A., Hartmann, S., & Ziegmann, G. (2022). Three-dimensional shear-angle determination with application to shear-frame test. Composite Structures, 285, 115134.
- Dawson, M. (2020). National Cybersecurity Education: Bridging Defense To Offense. Land Forces Academy Review Vol. XXV, No 1(97), 2020
- Kortjan, N., & Solms, R. Von. (2014). A Conceptual Framework for Cyber-Security Awareness and Education in SA. South African Computer Journal, 52, 29-41.
- Catota, F., E., Morgan, G., Sicker, D., C. (2019). Cybersecurity education in a developing nation: the Ecuadorian environment. Journal of Cybersecurity, 2019, 1–19 doi: 10.1093/cybsec/tyz001
- South African Government Gazette, (2015). National Cybersecurity Policy Framework for South Africa.
- Rahman, N. A. A, Sairi, I. H., Zizi, N. A. M., and Khalid, F. (2020) The Importance of Cybersecurity Education in School. International Journal of Information and Education Technology, Vol. 10, No. 5, May 2020
- Nakama, D., and Paullet, K. (2019). The urgency for cybersecurity education: The impact of early college innovation in Hawaii rural communities. Information System Education Journal, vol. 16, no. 4, pp. 41-52, 2019
- Khader, M., Karam, M.,Fares, H. (2021). Cybersecurity Awareness Framework for Academia. Information 2021, 12, 417. https://doi.org/ 10.3390/info12100417
- Mutemwa, M., Masango, M. G., & Gcaza, N. (2021, December). Managing the Shift in the Enterprise Perimeter in order to delay a Cybersecurity Breach. In Proceedings of the International Conference on Artificial Intelligence and its Applications (pp. 1-10).
- Aldawood H, Skinner G (2019). Reviewing Cyber Security Social Engineering Training and Awareness Programs—Pitfalls and Ongoing Issues. Future Internet 2019, 11(3), 73; https://doi.org/10.3390/fi11030073
- Bada, M., Sasse, A. M., Nurse, J. R. C. (2019). Cyber Security Awareness Campaigns: Why do they fail to change behaviour?
- Chowdhury, N., and Gkioulos, V. (2021). Cyber security training for critical infrastructure protection: A literature review. Computer Science Review .Volume 40, May 2021. https://doi.org/10.1016/j.cosrev.2021.100361
- Nguyen, T. T., & Reddi, V. J. (2021). Deep reinforcement learning for cyber security. IEEE Transactions on Neural Networks and Learning Systems.