EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 491 (2024) E3S Web Conf., 491 (2024) 04012 References
Open Access
Issue
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
Article Number 04012
Number of page(s) 10
Section Engineering for Environment Development Applications
DOI https://doi.org/10.1051/e3sconf/202449104012
Published online 21 February 2024
  1. M Ahmed, Q. Riaz, M. zeesan, H. Tahir, “Intrusion detection in internet of things using supervised Machine learning based on applications and Transport layer features using UNSW-NB-15 Dataset “, Published by Eurasip J WirelCommunNetw, 2021, https://doi.org/10.1186/s13638-021-018393-8 [Google Scholar]
  2. A. Adman, A. Muhammed, A.A.A. Ghani, A. Abdullah, F. Hakim, “An Intrusion detection system for the internet of things based on machine learning:Review and challenges, Symmetry (Basel) “ 13 (6),2021 1 13,https:??doi.org/10.3390/sym13061011 [Google Scholar]
  3. Y.K.Saheed, M.Azees, V.Chang, J.Deborah, B.balausamy, ”Efficient Cyber attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms”, 2021, published in IEEE Access 9,https://doi.org/10.1109/ACCESS.2021.3128837 [Google Scholar]
  4. G. Kavitha, N.M. Elango, “An approach to feature selection in intrusion detection systems using machine learning algorithms “,int. j. e-collaboration 16 (4), 2020 48-58,https://doi.org/10.4018/IJeC.2020100104 [CrossRef] [Google Scholar]
  5. Talukder, Md Alamin, KhondokarFida Hasan, Md Manowarul Islam, Md Ashraf Uddin, Arnisha Akhter, Mohammand Abu Yousuf, Fares Alharbi, and Mohammad Ali Moni. “A dependable hybrid machine learning model for network intrusion detection.” Journal of Information Security and Applications 72 (2023): 103405. [CrossRef] [Google Scholar]
  6. Enamul kabir, jiankun Hu, and Hua wang, ”A novel statistical technique for intrusion detection systems”, 2021 published by Taiyuvan Normal University. [Google Scholar]
  7. He, Ke, Dan Dongseong Kim, and Muhammad Rizwan Asghar. “Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey.” IEEE Communications Surveys & Tutorials (2023).Wilson, A. N., Gupta, K., Koduru, B. H., Kumar, A., Jha, A., &Cenkeramaddi, L. R. (2023). Recent Advances in Thermal Imaging and its Applications using Machine Learning: A Review. IEEE Sensors Journal. [Google Scholar]
  8. Sahani, N., Zhu, R., Cho, J. H., & Liu, C. C. (2023). “Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey”. ACM Transactions on Cyber-Physical Systems. [Google Scholar]
  9. Gutierrez-Garcia, J. L., Sanchez-DelaCruz, E., & Pozos-Parra, M. D. P. (2023, March).”A Review of Intrusion Detection Systems Using Machine Learning: Attacks, Algorithms and Challenges”. In Advances in Information and Communication: Proceedings of the 2023 Future of Information and Communication Conference (FICC) , Volume 2 (pp. 59-78). Cham: Springer Nature Switzerland [Google Scholar]
  10. Y.K. Saheed,Aremu Idris Abiodium,Sanjay Misra,”A machine learning-based intrusion detection for detecting internet of things network attacks”published by Elesiver 2022, https://doi.org/10.1016/j.aej.2022.02.063 [Google Scholar]
  11. Abdulla, Azeez Rahman, and Noor Ghazi M. Jameel. “A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms.” UHD Journal of Science and Technology 7, no. 1 (2023): 53-65. [CrossRef] [Google Scholar]
  12. Rajapaksha, Sampath, Harsha Kalutarage, M. Omar Al-Kadri, Andrei Petrovski, GarikayiMadzudzo, and Madeline Cheah. “AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey.” ACM Computing Surveys 55, no. 11 (2023): 1-40. [Google Scholar]
  13. Ramotsoela, D. T., Hancke, G. P., & Abu-Mahfouz, A. M. (2023).” Practical Challenges of Attack Detection in Microgrids Using Machine Learning.” Journal of Sensor and Actuator Networks, 12(1), 7. [CrossRef] [Google Scholar]
  14. Yi, L., Yin, M., &Darbandi, M. (2023).” A deep and systematic review of the intrusion detection systems in the fog environment”. Transactions on Emerging Telecommunications Technologies, 34(1), e4632. [CrossRef] [Google Scholar]
  15. Sharma, Hanjabam Saratchandra, MoirangthemMarjit Singh, and Arindam Sarkar. “Machine Learning-Based DoS Attack Detection Techniques in Wireless Sensor Network: A Review.” In Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, Volume 2, pp. 583-591. Singapore: Springer Nature Singapore, 2023. [Google Scholar]
  16. D’hooge, L., Verkerken, M., Wauters, T., De Turck, F., &Volckaert, B. (2023). “Investigating Generalized Performance of Data-Constrained Supervised Machine Learning Models on Novel, Related Samples in Intrusion Detection”. Sensors, 23(4), 1846. [CrossRef] [PubMed] [Google Scholar]
  17. Barkah, Azhari Shouni, Siti Rahayu Selamat, Zaheera Zainal Abidin, and Rizki Wahyudi. “Impact of Data Balancing and Feature Selection on Machine Learning-based Network Intrusion Detection.” JOIV: International Journal on Informatics Visualization 7, no. 1 (2023): 241-248. [CrossRef] [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.