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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
Cite this article
↗ https://www.doi.org/10.59256/ijire.20240503001References
1. A. Kumar, A. K. Singh, I. Ahmad et al., “A novel decentralized blockchain architecture for the preservation of privacy and data security
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
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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