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Research Article

Detection of Botnet in IOT Using Machine Learning

Vyshnav Unnikrishnan1 Jobin Mathew Samkutty2 Navin M Mathew3 Muhammad Shareef C S4 Chandu Asok5
1234 B. Tech, Computer Science, St. Thomas College of Engineering and Technology, Chengannur, Thrissur, Kerala, India. 5 Assistant Professor B. Tech, Computer Science, St. Thomas College of Engineering and Technology, Chengannur, Thrissur, Kerala, India.

Published Online: May-June 2024

Pages: 01-05

References

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against cyberattacks in healthcare,” Sensors, vol. 22, no. 15, pp. 1–14, 2022.
2. T. Alyas, I. Javed, A. Namoun, A. Tufail, S. Alshmrany, and N. Tabassum, “Live migration of virtual machines using a mamdani fuzzy
inference system,” Computers, Materials & Continua, vol. 71, no. 2, pp. 3019–3033, 2022.
3. M. S. Mazhar, Y. Saleem, A. Almogren et al., “Forensic analysis on internet of things (IoT) device using machine to machine (M2M)
framework,” Electronics, vol. 11, no. 7, p. 1126, 2022.
4. T. Alyas, K. Alissa, M. Alqahtani et al., “Multi-Cloud integration security framework using honeypots,” Mobile Information Systems,
vol. 2022, pp. 1–13, Article ID 2600712, 2022.
5. T. Kalsoom, N. Ramzan, S. Ahmed, and M. Ur-Rehman, “Advances in sensor technologies in the era of smart factory and industry 4.0,”
Sensors, vol. 20, p. 6783, 2020.
6. M. Ahmad, T. M Ghazal, and N. Aziz, “A survey on animal identification techniques past and present,” International Journal of
Computational and Innovative Sciences, vol. 1, no. 2, pp. 1–7, 2022.
7. ] P. Jacob, “UNSW-NB15 dataset feature selection and network intrusion detection using deep learning,” International Journal of
Recent Technology and Engineering, vol. 7, no. 5S2, 2019

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