Cite

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

A. Meryem and B.E. Ouahidi, “Hybrid intrusion detection system using machine learning,” Network Security, vol. 2020, no. 5, pp. 8-19, 2020. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar