References
- T. Heffernan, Ed., “Cyborg futures: cross-disciplinary perspectives on artificial intelligence and robotics” Cham: Springer International Publishing, 2019. doi: 10.1007/978-3-030-21836-2.
- Y.J. Gu, “The disembodiment of digital subject and the disappearance of women in the representations of cyborg, artificial intelligence, and posthuman,” Asian Women, vol. 36, no. 4, pp. 23-44, 2020.
- D. Li, “Blurring human and machine boundary-the post-humanist metaphor of cyborg-body in artificial intelligence and minority report,” in International Conference on Language, Art and Cultural Exchange (ICLACE), pp. 47-50, 2020.
- W. Shahid, Y. Li, D. Staples, G. Amin, S. Hakak and A. Ghorbani, “Are you a cyborg, bot or human? — a survey on detecting fake news spreaders,” IEEE Access, vol. 10, pp. 27069-27083, 2022.
- Y. Khan, S. Thakur, O. Obiyemi and E. Adetiba, “Identification of Bots and Cyborgs in the# FeesMustFall Campaign,” Informatics, vol. 9, no. 1, pp. 21, 2022.
- Z. Li, D. Zou, J. Tang, Z. Zhang, M. Sun et al. “A comparative study of deep learning-based vulnerability detection system,” IEEE Access, vol. 7, pp. 103184-103197, 2019.
- R. Russell, L. Kim, L. Hamilton, T. Lazovich, J. Harer et al. “Automated vulnerability detection in source code using deep representation learning,” in 17th IEEE international conference on machine learning and applications (ICMLA), Orlando, FL, USA, pp. 757-762, 2018.
- W. Wang, J. Song, G. Xu, Y. Li, H. Wang et al. “Contractward: Automated vulnerability detection models for ethereum smart contracts,” IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1133-1144, 2020.
- M. Goldhammer, S. Köhler, S. Zernetsch, K. Doll, B. Sick et al. “Intentions of vulnerable road users — detection and forecasting by means of machine learning,” Ieee Transactions on Intelligent Transportation Systems, vol. 21, no. 7, pp. 3035-3045, 2019.
- Z. Li, D. Zou, S. Xu, H. Jin, Y. Zhu et al. “Sysevr: A framework for using deep learning to detect software vulnerabilities,” IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 4, pp. 2244–2258, 2021.
- A. Meryem and B.E. Ouahidi, “Hybrid intrusion detection system using machine learning,” Network Security, vol. 2020, no. 5, pp. 8-19, 2020.
- O. Achbarou, M.A. El Kiram, O. Bourkoukou and S. Elbouanani, “A new distributed intrusion detection system based on multi-agent system for cloud environment,” International Journal of Communication Networks and Information Security, vol. 10, no. 3, pp. 526, 2018.
- D.A.A.G. Singh, R. Priyadharshini and E.J. Leavline, “Cuckoo optimization based intrusion detection system for cloud computing,” International Journal of Computer Network and Information Security, vol. 9, no. 11, pp. 42, 2018.
- M.A. Hatef, V. Shaker, M.R. Jabbarpour, J. Jung and H. Zarrabi, “HIDCC: A hybrid intrusion detection approach in cloud computing,” Concurrency and Computation: Practice and Experience, vol. 30, no. 3, pp. e4171, 2018.
- V. Chang, L. Golightly, P. Modesti, Q.A. Xu, L.M.T. Doan et al. “A survey on intrusion detection systems for fog and cloud computing,” Future Internet, vol. 14, no. 3, pp. 89, 2022.
- G. Luo, Z. Chen and B.O. Mohammed, “A systematic literature review of intrusion detection systems in the cloud‐based IoT environments,” Concurrency and Computation: Practice and Experience, vol. 34, no. 10, pp. e6822, 2022.
- S. Krishnaveni, S. Sivamohan, S. Sridhar and S. Prabhakaran, “Network intrusion detection based on ensemble classification and feature selection method for cloud computing,” Concurrency and Computation: Practice and Experience, vol. 34, no. 11, pp. e6838, 2022.
- S. Kannadhasan, R. Nagarajan and S. Thenappan, “Intrusion detection techniques based secured data sharing system for cloud computing using msvm,” in 9th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 50-56, 2022.
- W. Elmasry, A. Akbulut and A.H. Zaim, “A design of an integrated cloud-based intrusion detection system with third party cloud service,” Open Computer Science, vol. 11, no. 1, pp. 365-379, 2021.
- M.G. Raj and S.K. Pani, “A meta-analytic review of intelligent intrusion detection techniques in cloud computing environment,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 10, pp. 206-217, 2021.
- Z. Li, D. Zou, S. Xu, X. Ou, H. Jin et al. “Vuldeepecker: A deep learning-based system for vulnerability detection,” arXiv preprint arXiv:1801.01681, 2018, doi: /10.48550/arXiv.1801.01681
- S. Liu, G. Lin, Q.L. Han, S. Wen, J. Zhang et al. “DeepBalance: Deep-learning and fuzzy oversampling for vulnerability detection,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 7, pp. 1329-1343, 2019.