Open Access
ITM Web Conf.
Volume 63, 2024
1st International Conference on Advances in Machine Intelligence, and Cybersecurity Technologies (AMICT2023)
Article Number 01019
Number of page(s) 13
Published online 13 February 2024
  1. S. Dange and M. Chatterjee,“IoT botnet: The largest threat to the IoT network,” in Data Communication and Networks: Proceedings of GUCON 2019, Springer, 2019, pp. 137–157. [Google Scholar]
  2. R. Lakshmanan, “Researchers Uncover ‘Pink’ Botnet Malware That Infected Over 1.6 Million Devices.” Accessed: Nov. 14, 2023. [Online]. Available: [Google Scholar]
  3. ADMIN, “Russian Botnet Attack: Over 1 Million Devices Infected,” Dec. 14, 2021. Accessed: Nov. 14, 2023. [Online]. Available: [Google Scholar]
  4. H.-T. Nguyen, Q.-D. Ngo, and V.-H. Le, “A novel graph-based approach for IoT botnet detection,” Int. J. Inf. Secur., vol. 19, no. 5, pp. 567–577, Oct. 2020, doi: 10.1007/s10207-019-00475-6. [Google Scholar]
  5. A. Derhab, A. Aldweesh, A. Z. Emam, and F. A. Khan, “Intrusion detection system for internet of things based on temporal convolution neural network and efficient feature engineering,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1–16, 2020. [CrossRef] [Google Scholar]
  6. D. Kim, Y. Pan, and J. H. Park, “A study on the digital forensic investigation method of clever malware in IoT devices,” IEEE Access, vol. 8, pp. 224487–224499, 2020. [CrossRef] [Google Scholar]
  7. M. Ficco, “Detecting IoT malware by Markov chain behavioral models,” presented at the 2019 IEEE International Conference on Cloud Engineering (IC2E), IEEE, 2019, pp. 229–234. [CrossRef] [Google Scholar]
  8. R. Vinayakumar, M. Alazab, S. Srinivasan, Q.-V. Priam, S. K. Padannayil, and K. Simran, “A visualized botnet detection system based deep learning for the internet of things networks of smart cities,” IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4436–4456, 2020. [CrossRef] [Google Scholar]
  9. J. Jeon, J. H. Park, and Y.-S. Jeong, “Dynamic analysis for IoT malware detection with convolution neural network model,” IEEE Access, vol. 8, pp. 96899–96911, 2020. [CrossRef] [Google Scholar]
  10. S. Gaonkar, N. F. Dessai, J. Costa, A. Borkar, S. Aswale, and P. Shetgaonkar, “A survey on botnet detection techniques,” presented at the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), IEEE, 2020, pp. 1–6. [Google Scholar]
  11. A. O. Prokofiev, Y. S. Smirnova, and V. A. Surov, “A method to detect Internet of Things botnets,” presented at the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), IEEE, 2018, pp. 105–108. [CrossRef] [Google Scholar]
  12. W. Li, J. Jin, and J.-H. Lee, “Analysis of botnet domain names for IoT cybersecurity,” IEEE Access, vol. 7, pp. 94658–94665, 2019. [CrossRef] [Google Scholar]
  13. N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network intrusion detection for IoT security based on learning techniques,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, Art. no. 3, 2019. [Google Scholar]
  14. R. Heenan and N. Moradpoor, “Introduction to security onion,” 2016. [Google Scholar]
  15. A. Resmi and R. Manicka, “Intrusion detection system techniques and tools: A survey,” Scholars J. Eng. Technol., vol. 5, no. 3, pp. 122–130, 2017. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.