Exponential Squirrel Search Algorithm-Based Deep Classifier for Intrusion Detection in Cloud Computing with Big Data Assisted Spark Framework

References

• Abusitta, A., M. Bellaiche∗, M. Dagenais, and T. Halabi. 2019. A deep learning approach for proactive multi-cloud cooperativeintrusion detection system. Future Generation Computer Systems 98:308–18. doi:10.1016/j.future.2019.03.043.
   Web of Science ®Google Scholar
• Anand, S. 2020. Intrusion Detection System for Wireless Mesh Networks via Improved Whale Optimization. Journal of Networking and Communication Systems 3 (4):9–16.
   Google Scholar
• Bae, C., W. C. Yeh, M. A. Shukran, Y. Y. Chung, and T. J. Hsieh. 2012. A novel anomaly-network intrusion detection system using ABC algorithms. International Journal of Innovative Computing, Information and Control 8 (12):8231–48.
   Web of Science ®Google Scholar
• Bamakan, S. M. H., B. Amiri, M. Mirzabagheri, and Y. Shi. 2015. A new intrusion detection approach using PSO based multiple criteria linear programming. Procedia Computer Science 55:231–7.
   Google Scholar
• Bhaladhare, P. R., and D. C. Jinwala. 2014. A clustering approach for the-diversity model in privacy preserving data mining using fractional calculus-bacterial foraging optimization algorithm. Advances in Computer Engineering 2014:1–12. doi:10.1155/2014/396529.
   Google Scholar
• Chen, M., N. Wang, H. Zhou, and Y. Chen. 2018. FCM technique for efficient intrusion detection system for wireless networks in cloud environment. Computers & Electrical Engineering 71:978–87. doi:10.1016/j.compeleceng.2017.10.011.
   Web of Science ®Google Scholar
• Faker, O., and E. Dogdu. 2019. Intrusion detection using big data and deep learning techniques. In Proceedings of the 2019 ACM Southeast Conference, 86–93.
   Google Scholar
• Gali, M. 2021. Deep learning based optimization algorithm for cyber security intrusion detection system. Journal of Networking and Communication Systems 4 (2):24–30.
   Google Scholar
• Geeta, and Prakash, S. 2019. Role of virtualization techniques in cloud computing environment. In Advances in Computer Communication and Computational Sciences, 439–50. Singapore: Springer.
   Google Scholar
• Hajimirzaei, B., and N. J. Navimipour. 2019. Intrusion detection for cloud computing using neural networks andartificial bee colony optimization algorithm. ICT Express 5 (1):56–9. doi:10.1016/j.icte.2018.01.014.
   Web of Science ®Google Scholar
• Jaber, A. N., and S. U. Rehman. 2020. FCM–SVM based intrusion detection system for cloud computing environment. Cluster Computing 23 (4):3221. doi:10.1007/s10586-020-03082-6.
   Web of Science ®Google Scholar
• Jagdale, B., S. Sugave, and K. Kolhe. 2021. Design and analysis of fabrication threat management in peer-to-peer collaborative location privacy. International Journal of Computer Science & Network Security 21 (12):399–408.
   Web of Science ®Google Scholar
• Jain, M., V. Singh, and A. Rani. 2019. A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and Evolutionary Computation 44:148–75. doi:10.1016/j.swevo.2018.02.013.
   Web of Science ®Google Scholar
• Jayapriya, K., and N. A. B. Mary. 2019. Employing a novel 2-gram subgroup intra pattern (2GSIP) with stacked auto encoder for membrane protein classification. Molecular Biology Reports 46 (2):2259–72. doi:10.1007/s11033-019-04680-3.
   PubMed Web of Science ®Google Scholar
• Kanimozhi, V., and T. P. Jacob. 2019. Artificial Intelligence based Network Intrusion Detection withhyper-parameter optimization tuning on the realistic cyber datasetCSE-CIC-IDS2018 using cloud computing. In Proceedings of International Conference on Communication and Signal Processing (ICCSP), IEEE, 0033–6.
   Google Scholar
• Kavuri, S., G. R. Kancherla, and B. Brao. 2016. A novel hardware parameters based cloud data encryption and decryption against unauthorized users. Journal of Theoretical and Applied Information Technology 87 (2):291–9.
   Google Scholar
• Kavuri, S., and G. R. Kancherla. 2015. Cryptographic access control schemes in cloud storage services. Discovery 28 (103):34–9.
   Google Scholar
• Kim, J., N. Shin, S. Y. Jo, and S. H. Kim. 2017. Method of intrusion detection using deep neural network. In Proceedings of IEEE International Conference on Big Data and Smart Computing (BigComp), 313–6.
   Google Scholar
• Liu, G., H. Bao, and B. Han. 2018. A stacked auto encoder-based deep neural network for achieving gearbox fault diagnosis. Mathematical Problems in Engineering 2018:1–10. doi:10.1155/2018/5105709.
   Web of Science ®Google Scholar
• Manickam, M., and S. P. Rajagopalan. 2019. A hybrid multi-layer intrusion detection system in cloud. Cluster Computing 22 (S2):3961–9. doi:10.1007/s10586-018-2557-5.
   Google Scholar
• Milani, B. A., and N. J. Navimipour. 2016. A comprehensive review of the datareplicationtechniquesin the cloud environments: Major trends and future directions. Journal of Network and Computer Applications 64:229–38.
   Web of Science ®Google Scholar
• Milenkoski, A., M. Vieira, S. Kounev, A. AvritzeR, and B. D. Payne. 2015. Evaluating computer intrusion detection systems. ACM Computing Surveys 48 (1):1–41. doi:10.1145/2808691.
   Web of Science ®Google Scholar
• Navimipour, N. J., and F. S. Milani. 2015. Task scheduling in the cloud computing based on the cuckoo search algorithm. International Journal of Modeling and Optimization 5 (1):44–7.
   Google Scholar
• Nkikabahizi, C., W. Cheruiyot, and A. Kibe. 2017. Classification and analysis of techniques applied in intrusion detection systems. International Journal of Scientific Engineering and Technology 6 (7):216–9. doi:10.5958/2277-1581.2017.00023.7.
   Google Scholar
• Peng, K., V. C. M. Leung, and Q. Huang. 2018. Clustering approach based on mini batch k-means for intrusion detection system over big data. IEEE Access 6:11897–906. doi:10.1109/ACCESS.2018.2810267.
   Google Scholar
• Prasad, A. V. K. 2021. Deep learning based optimization for detection of attacks in IoT. Journal of Networking and Communication Systems 4 (2):31–6.
   Google Scholar
• Rabbani, M., Y. L. Wang, R. Khoshkangini, H. Jelodar, R. Zhao, and P. Hu. 2020. A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing. Journal of Network and Computer Applications 151:102507. doi:10.1016/j.jnca.2019.102507.
   Web of Science ®Google Scholar
• Saccucci, M. S., R. W. Amin, and J. M. Lucas. 1992. Exponentially weighted moving average controlschemes with variable sampling intervals. Communications in Statistics - Simulation and Computation 21 (3):627–57. doi:10.1080/03610919208813040.
   Web of Science ®Google Scholar
• Tang, C., N. Luktarhan, and Y. Zhao. 2020. SAAE-DNN: Deep learning method on intrusion detection. Symmetry 12 (10):1695. doi:10.3390/sym12101695.
   Google Scholar
• Veeraiah, N., and B. T. Krishna. 2018. Intrusion detection based on piecewise fuzzy C-means clustering and fuzzy naive Bayes rule. Multimedia Research 1 (1):27–32.
   Google Scholar
• Yin, H., M. Xue, Y. Xiao, K. Xia, and G. Yu. 2019. Intrusion detection classification model on animproved k-dependence Bayesian network. IEEE Access 7:157555–63. doi:10.1109/ACCESS.2019.2949890.
   Web of Science ®Google Scholar