Using a Multi- Layered Framework for Botnet Detection Based on Machine Learning Algorithms

Authors

  • Aditee Mattoo Assistant Professor & Dy. HoD, Department of Information Technology and M.Tech Integrated, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Soumya A. K. Assistant Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India
  • Vineet Saxena Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Manish Shrivastava Professor, Department of Computer Science & Engineering, Vivekananda Global University, Jaipur, India

Keywords:

Botnet, security, machine learning (ML), black hole optimized random forest (BHO-RF)

Abstract

By allowing attackers to take control of a significant amount of infected devices for illegal purposes, botnets pose severe challenges to network security. Due to the dynamic nature of botnet infrastructures and the advanced tactics used by attackers, identifying and preventing attacks by botnets is a complex undertaking. In this study, we present a novel machine learning (ML)-based black hole optimized random forest (BHO-RF) for botnet detection. The BHO method enhances the performance of the RF classifier by modifying the hyperparameters, boosting its ability to recognize botnet traffic. It is inspired by the behaviour of black holes in space. We have extensive tests utilizing the CTU-13 dataset to assess the efficacy of our suggested framework. The outcomes show that our multi-layered strategy outperforms conventional techniques, delivering higher accuracy, f1-score, precision, and recall in botnet identification. The approach also demonstrates robustness over noise and changes in transmission characteristics.

Downloads

Download data is not yet available.

References

Khan, R.U., Zhang, X., Kumar, R., Sharif, A., Golilarz, N.A. and Alazab, M., 2019. An adaptive multi-layer botnet detection technique using machine learning classifiers. Applied Sciences, 9(11), p.2375.

Ibrahim, W.N.H., Anuar, S., Selamat, A., Krejcar, O., Crespo, R.G., Herrera-Viedma, E. and Fujita, H., 2021. Multilayer framework for botnet detection using machine learning algorithms. IEEE Access, 9, pp.48753-48768.

Vinayakumar, R., Alazab, M., Srinivasan, S., Pham, Q.V., Padannayil, S.K. and Simran, K., 2020. A visualized botnet detection system based on deep learning for the internet of things networks of smart cities. IEEE Transactions on Industry Applications, 56(4), pp.4436-4456.

Alqatawna, J.F., Ala’M, A.Z., Hassonah, M.A. and Faris, H., 2021. Android botnet detection using machine learning models based on a comprehensive static analysis approach. Journal of Information Security and Applications, 58, p.102735.

Shinan, K., Alsubhi, K., Alzahrani, A. and Ashraf, M.U., 2021. Machine learning-based botnet detection in software-defined network: a systematic review. Symmetry, 13(5), p.866.

Letteri, I., Della Penna, G. and De Gasperis, G., 2018. Botnet detection in software defined networks by deep learning techniques. In Cyberspace Safety and Security: 10th International Symposium, CSS 2018, Amalfi, Italy, October 29–31, 2018, Proceedings 10 (pp. 49-62). Springer International Publishing.

Costa, V.G.T.D., Barbon, S., Miani, R.S., Rodrigues, J.J. and Zarpelão, B.B., 2019. Mobile botnets detection based on machine learning over system calls. International Journal of Security and Networks, 14(2), pp.103-118.

Gadelrab, M.S., ElSheikh, M., Ghoneim, M.A. and Rashwan, M., 2018. BotCap: Machine learning approach for botnet detection based on statistical features. Int. J. Commun. Netw. Inf. Secur, 10(3), p.563.

Stojanovic, N. . (2020). Deep Learning Technique-Based 3d Lung Image-Based Tumor Detection Using segmentation and Classification. Research Journal of Computer Systems and Engineering, 1(2), 13:19. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/6

Shende, P. ., Vishal Ashok, W. ., Limkar, S. ., D. Kokate, M. ., Lavate, S. ., & Khedkar, G. . (2023). Assessment of Seismic Hazards in Underground Mine Operations using Machine Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 237–243. https://doi.org/10.17762/ijritcc.v11i2s.6142

Gaonkar, S., Dessai, N.F., Costa, J., Borkar, A., Aswale, S. and Shetgaonkar, P., 2020, February. A survey on botnet detection techniques. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-6). IEEE.

Soe, Y.N., Feng, Y., Santosa, P.I., Hartanto, R. and Sakurai, K., 2020. Machine learning-based IoT-botnet attack detection with sequential architecture. Sensors, 20(16), p.4372.

Moorthy, R.S.S. and Nathiya, N., 2023. Botnet Detection Using Artificial Intelligence. Procedia Computer Science, 218, pp.1405-1413.

Wai, F.K., Lilei, Z., Wai, W.K., Le, S. and Thing, V.L., 2018, October. Automated botnet traffic detection via machine learning. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 0038-0043). IEEE.

Letteri, I., Penna, G.D. and Gasperis, G.D., 2019. Security in the internet of things: botnet detection in software-defined networks by deep learning techniques. International Journal of High Performance Computing and Networking, 15(3-4), pp.170-182.

Yang, X., Guo, Z. and Mai, Z., 2022, July. Botnet Detection Based on Machine Learning. In 2022 International Conference on Blockchain Technology and Information Security (ICBCTIS) (pp. 213-217). IEEE.

Koroniotis, N., Moustafa, N., Sitnikova, E. and Slay, J., 2018. Towards developing network forensic mechanism for botnet activities in the IoT based on machine learning techniques. In Mobile Networks and Management: 9th International Conference, MONAMI 2017, Melbourne, Australia, December 13-15, 2017, Proceedings 9 (pp. 30-44). Springer International Publishing.

Downloads

Published

11.07.2023

How to Cite

Mattoo, A. ., A. K., S. ., Saxena, V. ., & Shrivastava, M. . (2023). Using a Multi- Layered Framework for Botnet Detection Based on Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 49–54. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3020

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.