Virtual Machine Migration-Based Intrusion Detection System in Cloud Environment Using Deep Recurrent Neural Network

References

• Abdalla, M, and A. M. Ahmed. 2020. Optimization driven mapreduce framework for indexing and retrieval of big data. Ksii Transactions on Internet and Information SYSTEMS 14:1886–908.
   Web of Science ®Google Scholar
• Agrawal, B., T. Wiktorski, and C. Rong. 2016. Adaptive anomaly detection in cloud using robust and scalable principal component analysis. In IEEE 15th International Symposium on Parallel and Distributed Computing (ISPDC), 100–6.
   Google Scholar
• Agrawal, B., T. Wiktorski, and C. Rong. 2017. Adaptive real‐time anomaly detection in cloud infrastructures. Concurrency and Computation: Practice and Experience 29 (24):e4193. doi:10.1002/cpe.4193.
   Web of Science ®Google Scholar
• Alabdulatif, A., H. Kumarage, I. Khalil, and X. Yi. 2017. Privacy-preserving anomaly detection in cloud with lightweight homomorphic encryption. Journal of Computer and System Sciences 90:28–45. doi:10.1016/j.jcss.2017.03.001.
   Web of Science ®Google Scholar
• Alharbi, A., W. Alosaimi, H. Alyami, H. T. Rauf, and R. Damaševičius. 2021. Botnet attack detection using local global best bat algorithm for industrial internet of things. Electronics 10 (11):1341. doi:10.3390/electronics10111341.
   Web of Science ®Google Scholar
• Ali, M. H., M. M. Jaber, S. K. Abd, A. Rehman, M. J. Awan, R. Damaševičius, and S. A. Bahaj. 2022. Threat analysis and distributed denial of service (DDoS) attack recognition in the Internet of Things (IoT). Electronics 11 (3):494., doi:10.3390/electronics11030494.
   Web of Science ®Google Scholar
• AlKadi, O., N. Moustafa, B. Turnbull, and K.-K. R. Choo. 2019. Mixture localization-based outliers models for securing data migration in cloud centers. IEEE Access. 7:114607–18. doi:10.1109/ACCESS.2019.2935142.
   Web of Science ®Google Scholar
• Alzaqebah, A., I. Aljarah, O. Al-Kadi, and R. Damaševičius. 2022. A modified grey wolf optimization algorithm for an intrusion detection system. Mathematics 10 (6):999. doi:10.3390/math10060999.
   Web of Science ®Google Scholar
• Borkar, G. M, and A. R. Mahajan. 2017. A secure and trust based on-demand multipath routing scheme for self-organized mobile ad-hoc networks. Wireless Networks 23 (8):2455–72. doi:10.1007/s11276-016-1287-y.
   Web of Science ®Google Scholar
• Calheiros, R. N., K. Ramamohanarao, R. Buyya, C. Leckie, and S. Versteeg. 2017. On the effectiveness of isolation‐based anomaly detection in cloud data centers. Concurrency and Computation: Practice and Experience 29 (18):e4169. doi:10.1002/cpe.4169.
   Web of Science ®Google Scholar
• Chen, G., G. Sun, Y. Xu, and B. Long. 2009. Integrated research of parallel computing: Status and future. Science Bulletin 54 (11):1845–53. doi:10.1007/s11434-009-0261-9.
   Web of Science ®Google Scholar
• Chen, M., Y. Qian, J. Chen, K. Hwang, S. Mao, and L. Hu. 2016. Privacy protection and intrusion avoidance for cloudlet-based medical data sharing. IEEE Transactions on Cloud Computing 8 (4):1274–1283.
   Google Scholar
• Dataset: BoT-IoT dataset. 2020. https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/bot_iot.php
   Google Scholar
• Dhage, S. N, and B. B. Meshram. 2012. Intrusion detection system in cloud computing environment. International Journal of Cloud Computing 1 (2/3):261–82. doi:10.1504/IJCC.2012.046711.
   Google Scholar
• Farshchi, M., J. G. Schneider, I. Weber, and J. Grundy. 2018. Metric selection and anomaly detection for cloud operations using log and metric correlation analysis. Journal of Systems and Software 137:531–49. doi:10.1016/j.jss.2017.03.012.
   Web of Science ®Google Scholar
• Fu, S., J. Liu, and H. Pannu. 2012. A hybrid anomaly detection framework in cloud computing using one-class and two-class support vector machines. In International Conference on Advanced Data Mining and Applications, Springer, Berlin, Heidelberg, 726–38.
   Google Scholar
• Ganeshkumar, P, and N. Pandeeswari. 2016. Adaptive neuro-fuzzy-based anomaly detection system in cloud. International Journal of Fuzzy Systems 18 (3):367–78. doi:10.1007/s40815-015-0080-x.
   Web of Science ®Google Scholar
• Garg, S., K. Kaur, N. Kumar, S. Batra, and M. S. Obaidat. 2018. HyClass: Hybrid classification model for anomaly detection in cloud environment. In IEEE International Conference on Communications (ICC), 1–7.
   Google Scholar
• Hui, Y. 2018. A virtual machine anomaly detection system for cloud computing infrastructure. The Journal of Supercomputing 74 (11):6126–34. doi:10.1007/s11227-018-2518-z.
   Web of Science ®Google Scholar
• Karuppusamy, L., J. Ravi, M. Dabbu, and S. Lakshmanan. 2022. Chronological salp swarm algorithm based deep belief network for intrusion detection in cloud using fuzzy entropy. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 35 (1):1–19. doi:10.1002/jnm.2948.
   Web of Science ®Google Scholar
• Koroniotis, N., N. Moustafa, E. Sitnikova, and B. Turnbull. 2019. Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset. Future Generation Computer Systems 100:779–96. doi:10.1016/j.future.2019.05.041.
   Web of Science ®Google Scholar
• Lim, D., Y. S. Ong, Y. Jin, B. Sendhoff, and B. S. Lee. 2007. Efficient hierarchical parallel genetic algorithms using grid computing. Future Generation Computer Systems 23 (4):658–70. doi:10.1016/j.future.2006.10.008.
   Web of Science ®Google Scholar
• Liu, D., Z. Wu, and S. Sun. 2022. Study on Subway passenger flow prediction based on deep recurrent neural network. Multimedia Tools and Applications 81 (14):18979–92. doi:10.1007/s11042-020-09088-x.
   Web of Science ®Google Scholar
• Mahmood, Z., C. Agrawal, S. S. Hasan, and S. Zenab. 2012. Intrusion detection in cloud computing environment using neural network. International Journal of Research in Computer Engineering and Electronics 1 (1):1–4.
   Google Scholar
• Mekhmoukh, A, and K. Mokrani. 2015. Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation. Computer Methods and Programs in Biomedicine 122 (2):266–81. doi:10.1016/j.cmpb.2015.08.001.
   PubMed Web of Science ®Google Scholar
• Meng, X., Y. Liu, X. Gao, and H. Zhang. 2014. A new bio-inspired algorithm: chicken swarm optimization. In International conference in swarm intelligence, Springer, Cham, 86–94.
   Google Scholar
• Mirjalili, S, and A. Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software 95:51–67. doi:10.1016/j.advengsoft.2016.01.008.
   Web of Science ®Google Scholar
• Mohsin, M., H. Li, and H. B. Abdalla. 2020. Optimization driven Adam-Cuckoo search-based deep belief network classifier for data classification. IEEE Access. 8:105542–60. doi:10.1109/ACCESS.2020.2999865.
   Web of Science ®Google Scholar
• Pandeeswari, N, and G. Kumar. 2016. Anomaly detection system in cloud environment using fuzzy clustering based ANN. Mobile Networks and Applications 21 (3):494–505. doi:10.1007/s11036-015-0644-x.
   Web of Science ®Google Scholar
• Sabahi, F. 2011. Cloud computing security threats and responses. In IEEE 3rd International Conference on Communication Software and Networks, Xi'an, China, 245–249.
   Google Scholar
• Shadravan, S., H. R. Naji, and V. K. Bardsiri. 2019. The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial Intelligence 80:20–34. doi:10.1016/j.engappai.2019.01.001.
   Web of Science ®Google Scholar
• Soldatos, J., M. Serrano, and M. Hauswirth. 2012. Convergence of utility computing with the internet-of-things. In IEEE Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Palermo, Italy, 874–849.
   Google Scholar
• Vieira, K., A. Schulter, C. Westphall, and C. Westphall. 2010. Intrusion detection for grid and cloud computing. IT Professional 12( (4):38–43. doi:10.1109/MITP.2009.89.
   Web of Science ®Google Scholar
• Ye, X., X. Chen, H. Wang, X. Zeng, G. Shao, X, and Yin, C. X. 2016. An anomalous behavior detection model in cloud computing. Tsinghua Science and Technology 21 (3):322–32. doi:10.1109/TST.2016.7488743.
   Web of Science ®Google Scholar
• Zarrabi, A, and A. Zarrabi. 2012. Internet intrusion detection system service in a cloud. International Journal of Computer Science Issues (IJCSI) 9 (5):308.
   Google Scholar
• Zhang, Z., J. Wen, J. Zhang, X. Cai, and L. Xie. 2020. A many objective-based feature selection model for anomaly detection in cloud environment. IEEE Access. 8:60218–31. doi:10.1109/ACCESS.2020.2981373.
   Web of Science ®Google Scholar