Exploring Deep Learning Techniques in Cloud Computing to Detect Malicious Network Traffic: A Sustainable Computing Approach

Автор: Nagesh Shenoy H., K. R. Anil Kumar, Suchitra N. Shenoy, Abhishek S. Rao, Rajgopal K.T.

Журнал: International Journal of Wireless and Microwave Technologies @ijwmt

Статья в выпуске: 5 Vol.11, 2021 года.

Бесплатный доступ

The demand for cloud computing systems has increased tremendously in the IT sector and various business applications due to their high computation and cost-effective solutions to various computing problems. This increased demand has raised several challenges such as load balancing and security in cloud systems. Numerous approaches have been presented for load balancing but providing security and maintaining integrity and privacy remains a less explored research area. Intrusion detection systems have emerged as a promising solution to predict attacks. In this work, we develop a deep learning-based scheme that contains data pre-processing, convolution operations, BiLSTM model, attention layer, and CRF modeling. The current study employs a machine learning-based approach to detect intrusions based on the attackers' historical behavior. Deep learning algorithms were used to extract features from the image and determine the significance of dense packets to generate the salient fine-grained feature that can be used to detect malicious traffic and presents the final classification using fused features.


Cloud Computing, Load Balancing, Intrusion Detection, Convolution Neural Network, Cloud Security

Короткий адрес: https://readera.org/15017693

IDR: 15017693   |   DOI: 10.5815/ijwmt.2021.05.02

Список литературы Exploring Deep Learning Techniques in Cloud Computing to Detect Malicious Network Traffic: A Sustainable Computing Approach

  • Arunarani, A. R., Manjula, D., & Sugumaran, V. (2019). Task scheduling techniques in cloud computing: A literature survey. Future Generation Computer Systems, 91, 407-415.
  • Shahidinejad, A., Ghobaei-Arani, M., & Masdari, M. (2020). Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Cluster Computing, 1-24.
  • Gochhait, S., Butt, S. A., Jamal, T., & Ali, A. (2020). Cloud enhances agile software development. In Cloud Computing Applications and Techniques for E-Commerce (pp. 28-49). IGI Global.
  • Kumar, M., Sharma, S. C., Goel, A., & Singh, S. P. (2019). A comprehensive survey for scheduling techniques in cloud computing. Journal of Network and Computer Applications, 143, 1-33.
  • Elsedimy, E. I., & Algarni, F. (2021). Toward Enhancing the Energy Efficiency and Minimizing the SLA Violations in Cloud Data Centers. Applied Computational Intelligence and Soft Computing, 2021.
  • Ali, M. B. (2021). Multi-Perspectives of Cloud Computing Service Adoption Quality and Risks in Higher Education. In Handbook of Research on Modern Educational Technologies, Applications, and Management (pp. 1-19). IGI Global.
  • Lee, I. (2021). Pricing and Profit Management Models for SaaS Providers and IaaS Providers. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 859-873.
  • Kim, M. H., Lee, J. Y., Shah, S. A. R., Kim, T. H., & Noh, S. Y. (2021). Min-max exclusive virtual machine placement in cloud computing for scientific data environment. Journal of Cloud Computing, 10(1), 1-17.
  • Nagesh Shenoy H, K R Anil Kumar, Rajgopal K T and Abhishek S. Rao (2020). An Audit on Cloud Architectures Addressing Data Privacy and Security Concerns. International Journal of Advanced Science and Technology, Vol. 29, No. 6, (2020), pp. 6373 – 6382.
  • Shyla, S. I., & Sujatha, S. S. (2020). Cloud Security: LKM and Optimal Fuzzy System for Intrusion Detection in Cloud Environment. Journal of Intelligent Systems, 29(1), 1626-1642.
  • Gassais, R., Ezzati-Jivan, N., Fernandez, J. M., Aloise, D., & Dagenais, M. R. (2020). Multi-level host-based intrusion detection system for Internet of things. Journal of Cloud Computing, 9(1), 1-16.
  • Bedi, P., Gupta, N., & Jindal, V. (2021). I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems. Applied Intelligence, 51(2), 1133-1151.
  • Liu, J., Zhang, W., Ma, T., Tang, Z., Xie, Y., Gui, W., & Niyoyita, J. P. (2020). Toward security monitoring of industrial cyber-physical systems via hierarchically distributed intrusion detection. Expert Systems with Applications, 158, 113578.
  • Roschke, S., Cheng, F., & Meinel, C. (2009, December). Intrusion detection in the cloud. In 2009 eighth IEEE international conference on dependable, autonomic and secure computing (pp. 729-734). IEEE.
  • Abusitta, A., Bellaiche, M., Dagenais, M., & Halabi, T. (2019). A deep learning approach for proactive multi-cloud cooperative intrusion detection system. Future Generation Computer Systems, 98, 308-318.
  • Song, H. M., Woo, J., & Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21, 100198.
  • Raja, S., & Ramaiah, S. (2017). An efficient fuzzy-based hybrid system to cloud intrusion detection. International Journal of Fuzzy Systems, 19(1), 62-77.
  • Gao, Y., Liu, Y., Jin, Y., Chen, J., & Wu, H. (2018). A novel semi-supervised learning approach for network intrusion detection on cloud-based robotic system. IEEE Access, 6, 50927-50938.
  • Hajimirzaei, B., & Navimipour, N. J. (2019). Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express, 5(1), 56-59.
  • Jaber, A. N., & Rehman, S. U. (2020). FCM–SVM based intrusion detection system for cloud computing environment. Cluster Computing, 1-11.
  • Manickam, M., Ramaraj, N., & Chellappan, C. (2019). A combined PFCM and recurrent neural network-based intrusion detection system for cloud environment. International Journal of Business Intelligence and Data Mining, 14(4), 504-527.
  • Besharati, E., Naderan, M., & Namjoo, E. (2019). LR-HIDS: logistic regression host-based intrusion detection system for cloud environments. Journal of Ambient Intelligence and Humanized Computing, 10(9), 3669-3692.
  • Wang, W., Ren, L., Chen, L., & Ding, Y. (2019). Intrusion detection and security calculation in industrial cloud storage based on an improved dynamic immune algorithm. Information Sciences, 501, 543-557.
  • Wang, W., Du, X., Shan, D., Qin, R., & Wang, N. (2020). Cloud intrusion detection method based on stacked contractive auto-encoder and support vector machine. IEEE Transactions on Cloud Computing.
  • Krishnaveni, S., Sivamohan, S., Sridhar, S. S., & Prabakaran, S. (2021). Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing. Cluster Computing, 1-19.
  • Nagesh Shenoy H, K. R. Anil Kumar, Suchitra N Shenoy, & Abhishek S. Rao (2020). "Data Security in Cloud Environment Based on Comparative Performance Evaluation of Cryptographic Algorithms". International Journal of Advanced Trends in Computer Science and Engineering, Volume 9, No.4, July – August 2020.
  • Rao, A. S., Sandhya S, Anusha K, Arpitha, Chandana Nayak, Meghana, Sneha Nayak. (2020). Exploring Deep Learning Techniques for Kannada Handwritten Character Recognition: A Boon for Digitization. International Journal of Advanced Science and Technology, 29(5), 11078-11093.
  • Rao, A. S., Kamath, B. S., Ramya, R., Chowdhury, S., Shreya, A., & Pattan, R. K. K. (2020). Use of Artificial Neural Network in Developing a Personality Prediction Model for Career Guidance: A Boon for Career Counselors. International Journal of Control and Automation, 13(4), 391-400.
  • Rao, A. S., Aruna Kumar, S. V., Jogi, P., Chinthan Bhat, K., Kuladeep Kumar, B., & Gouda, P. Student Placement Prediction Model: A Data Mining Perspective for Outcome-Based Education System. International Journal of Recent Technology and Engineering (IJRTE), 8, 2497-2507.
  • Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7, 41525-41550.
Статья научная