Enhancing Cloud Data Center Security through Deep Learning: A Comparative Analysis of RNN, CNN, and LSTM Models for Anomaly and Intrusion Detection
Received: 30 October 2024 | Revised: 24 November 2024, 28 November 2024, and 3 December 2024 | Accepted: 22 December 2024 | Online: 8 January 2025
Corresponding author: Shimaa A. Ahmed
Abstract
Cloud data centers form the backbone of modern digital ecosystems, enabling critical operations for businesses, governments, and individuals around the world. However, their high connectivity and complexity make them prime targets for cyberattacks, leading to service disruptions and data breaches. This paper investigates the use of deep learning techniques, namely Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks, to enhance cloud data center security. By employing these models for anomaly detection and intrusion prevention, the study performs a comparative analysis. The results indicate that the LSTMs achieved the highest ROC AUC score (0.90), demonstrating better detection of persistent threats. These findings highlight the potential of deep learning to revolutionize cloud security by providing scalable, accurate, and proactive measures against evolving cyber threats.
Keywords:
cloud data centers, deep learning, downtime, cyberattacks, predictive analytics, anomaly detection, intrusion preventionDownloads
References
"Cost of a data breach 2023," IBM. https://www.ibm.com/reports/data-breach.
S. G. Kene and D. P. Theng, "A review on intrusion detection techniques for cloud computing and security challenges," in 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, Feb. 2015, pp. 227–232.
L. Abrams, "Over 500,000 Zoom accounts sold on hacker forums, the dark web," BleepingComputer. https://www.bleepingcomputer.com/news/security/over-500-000-zoom-accounts-sold-on-hacker-forums-the-dark-web/.
V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: A survey," ACM Computing Surveys, vol. 41, no. 3, Apr. 2009.
I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, "Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection," IEEE Access, vol. 6, pp. 33789–33795, 2018.
Z. Chkirbene, A. Erbad, R. Hamila, A. Gouissem, A. Mohamed, and M. Hamdi, "Machine Learning Based Cloud Computing Anomalies Detection," IEEE Network, vol. 34, no. 6, pp. 178–183, Nov. 2020.
S. Rahman, S. Pal, S. Mittal, T. Chawla, and C. Karmakar, "SYN-GAN: A robust intrusion detection system using GAN-based synthetic data for IoT security," Internet of Things, vol. 26, Jul. 2024, Art. no. 101212.
B. Mopuru and Y. Pachipala, "Enhanced Intrusion Detection in IoT with a Novel PRBF Kernel and Cloud Integration," Engineering, Technology & Applied Science Research, vol. 14, no. 4, pp. 14988–14993, Aug. 2024.
A. A. Alhashmi, A. A. Darem, A. B. Alshammari, L. A. Darem, H. K. Sheatah, and R. Effghi, "Ransomware Early Detection Techniques," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14497–14503, Jun. 2024.
S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, "Unsupervised real-time anomaly detection for streaming data," Neurocomputing, vol. 262, pp. 134–147, Nov. 2017.
J. B. Awotunde, C. Chakraborty, and A. E. Adeniyi, "Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection," Wireless Communications and Mobile Computing, vol. 2021, no. 1, 2021, Art. no. 7154587.
T. A. Devi and A. Jain, "Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud Computing Environments," in 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India, May 2024, pp. 541–546.
I. Ullah and Q. H. Mahmoud, "Design and Development of RNN Anomaly Detection Model for IoT Networks," IEEE Access, vol. 10, pp. 62722–62750, 2022.
B. Lindemann, B. Maschler, N. Sahlab, and M. Weyrich, "A survey on anomaly detection for technical systems using LSTM networks," Computers in Industry, vol. 131, Oct. 2021, Art. no. 103498.
L. Mohammadpour, T. C. Ling, C. S. Liew, and A. Aryanfar, "A Survey of CNN-Based Network Intrusion Detection," Applied Sciences, vol. 12, no. 16, Jan. 2022, Art. no. 8162.
M. Nour and J. Slay, "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)," in 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, Nov. 2015, pp. 1–6.
M. S. Al-Daweri, K. A. Zainol Ariffin, S. Abdullah, and M. F. E. Md. Senan, "An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System," Symmetry, vol. 12, no. 10, p. 1666, Oct. 2020.
D. M. W. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." arXiv, Oct. 11, 2020.
M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, Jul. 2009, pp. 1–6.
F. M. Shiri, T. Perumal, N. Mustapha, and R. Mohamed, "A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU." arXiv, Oct. 24, 2024.
J. Brownlee, "How to Grid Search Hyperparameters for Deep Learning Models in Python with Keras," MachineLearningMastery.com, 2022. https://www.machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/.
Downloads
How to Cite
License
Copyright (c) 2024 Shimaa A. Ahmed, Entisar H. Khalifa, Majid Nawaz, Faroug A. Abdalla, Ashraf F. A. Mahmoud
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
