AI-Driven Security Paradigms: Elevating Cloud Protection with Machine Learning

Authors

  • Bushra Majeed Muter Department of Specialist Supervision in Wasit Education, Iraq. Author
  • Ayat Jasim Mohamed Al-Amarah University College, Department: Medical instrumentation Techniques, Iraq. Author

DOI:

https://doi.org/10.61856/78jczn59

Keywords:

LSTM networks, Network intrusion detection, Behavior-Centric Cybersecurity Center, BCCC dataset, Cybersecurity

Abstract

In the literature, some studies have explored classifying network traffic using Long Short-Term Memory (LSTM) networks to enhance cloud security. We analyzed a dataset—BCCC—that includes various types of network traffic: Benign, Benign-Email-Receive, Benign-Email-Send, Benign-FTP, Benign-SSH, Benign-Systemic, Benign-Telnet, and Benign-Web_Browsing_HTTP-S. Key features examined include fwd_ack_flag_percentage_in_fwd_packets, fwd_ack_flag_percentage_in_total, min_fwd_header_bytes_delta_len, and handshake_duration. The model performed well in detecting the Benign class, but some classes with fewer samples, such as Benign-FTP and Benign-Email-Receive, require improved precision and recall due to class imbalance. Overall, the model’s performance in classifying network traffic is strong. This research outlines strategies for addressing class imbalance and refining feature engineering. It provides a foundation for further, more detailed investigations into AI approaches for network traffic classification, highlighting the importance of sample balancing to achieve high accuracy.

 

References

Alauthman, M., Aslam, N., Al-kasassbeh, M., Khan, S., Al-Qerem, A., & Raymond Choo, K.-K. (2020, 2020/01/15/). An efficient reinforcement learning-based Botnet detection approach. Journal of Network and Computer Applications, 150, 102479. https://doi.org/https://doi.org/10.1016/j.jnca.2019.102479

Aleesa, A. M., Zaidan, B. B., Zaidan, A. A., & Sahar, N. M. (2020, 2020/07/01). Review of intrusion detection systems based on deep learning techniques: coherent taxonomy, challenges, motivations, recommendations, substantial analysis and future directions. Neural Computing and Applications, 32(14), 9827-9858. https://doi.org/10.1007/s00521-019-04557-3

Emmanni, P. S. (2024). Leveraging Artificial Intelligence and Machine Learning for Threat Detection in Hybrid Cloud Systems. International Journal of Artificial Intelligence & Machine Learning (IJAIML), 3 (1), 75-84. https://doi.org/https://doi.org/10.17605/OSF.IO/HUXK8

Gauthama Raman, M. R., Somu, N., Jagarapu, S., Manghnani, T., Selvam, T., Krithivasan, K., & Shankar Sriram, V. S. (2020, 2020/06/01). An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm. Artificial Intelligence Review, 53(5), 3255-3286. https://doi.org/10.1007/s10462-019-09762-z

Hussain, A., & Shabir, G. (2024). AI-Powered DevSecOps: Elevating Security Practices with Machine Learning. https://rgdoi.net/10.13140/RG.2.2.25673.76640

Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020, 2020/03/01/). Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Systems with Applications, 141, 112963. https://doi.org/https://doi.org/10.1016/j.eswa.2019.112963

Maalem Lahcen, R. A., Caulkins, B., Mohapatra, R., & Kumar, M. (2020, 2020/04/21). Review and insight on the behavioral aspects of cybersecurity. Cybersecurity, 3(1), 10. https://doi.org/10.1186/s42400-020-00050-w

Sarker, I. H. (2021, 2021/03/22). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Sarker, I. H., Furhad, M. H., & Nowrozy, R. (2021, 2021/03/26). AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions. SN Computer Science, 2(3), 173. https://doi.org/10.1007/s42979-021-00557-0

Sarker, I. H., & Kayes, A. S. M. (2020, 2020/10/15/). ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services. Journal of Network and Computer Applications, 168, 102762. https://doi.org/https://doi.org/10.1016/j.jnca.2020.102762

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., Roepman, R., Dietmann, S., Virta, M., Kengara, F., Zhang, Z., Zhang, L., Zhao, T., Dai, J., Yang, J., Lan, L., Luo, M., Liu, Z., An, T., Zhang, B., He, X., Cong, S., Liu, X., Zhang, W., Lewis, J. P., Tiedje, J. M., Wang, Q., An, Z., Wang, F., Zhang, L., Huang, T., Lu, C., Cai, Z., Wang, F., & Zhang, J. (2021, 2021/11/28/). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179. https://doi.org/https://doi.org/10.1016/j.xinn.2021.100179

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Published

09/15/2024

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المقالات

How to Cite

Muter, B. M., & Mohamed, . A. J. . (2024). AI-Driven Security Paradigms: Elevating Cloud Protection with Machine Learning. International Innovations Journal of Applied Science, 1(2). https://doi.org/10.61856/78jczn59

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