Open Access
| Issue |
ITM Web Conf.
Volume 85, 2026
Intelligent Systems for a Sustainable Future (ISSF 2026)
|
|
|---|---|---|
| Article Number | 02003 | |
| Number of page(s) | 6 | |
| Section | Cybersecurity, Blockchain & Threat Intelligence | |
| DOI | https://doi.org/10.1051/itmconf/20268502003 | |
| Published online | 09 April 2026 | |
- S. U. Khan, N. Khan, F. U. M. Ullah, M. J. Kim, M. Y. Lee, and S. W. Baik, "Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting," Energy Buildings, vol. 279, Art. no. 112705, 2023, doi: 10.1016/j.enbuild.2022.112705. [Google Scholar]
- R. Ch, S. Nimmala, I. Batra, A. Malik, and P. K. Malik, "Enhancing cloud security and efficiency through AI-driven intrusion detection and machine learning-based resource management," in Deep Learning Innovations for Securing Critical Infrastructures, 2025, pp. 239–254. [Google Scholar]
- R. Ch, U. Naresh, A. Malik, and M. P. S. Hattamurrahman, "Deep learning approach for breast cancer detection using UNet and CNN in ultrasound imaging," Engineering Proceedings, vol. 107, no. 1, p. 77, 2025. (Demonstrates advanced deep learning analytics applicable to intelligent decision systems.) [Google Scholar]
- R. Ch, P. Sudheer, I. Batra, and F. Sembiring, "A novel adaptive cluster-based federated learning framework for anomaly detection in VANETs," Engineering Proceedings, vol. 107, no. 1, p. 79, 2025. [Google Scholar]
- S. Myeong, M. J. Ahn, Y. Kim, S. Chu, and W. Suh, "Government data performance: The roles of technology, government capacity, and globalization through the effects of national innovativeness," Sustainability, vol. 13, no. 22, Art. no. 12589, 2021, doi: 10.3390/su132212589. [Google Scholar]
- C. Wang, E. Steinfeld, J. L. Maisel, and B. Kang, "Is your smart city inclusive? Evaluating proposals from the U.S. Department of Transportation's Smart City Challenge," Sustain. Cities Soc., vol. 74, Art. no. 103148, 2021, doi: 10.1016/j.scs.2021.103148. [Google Scholar]
- K. Kourtit, M. M. M. Pele, P. Nijkamp, and D. T. Pele, "Safe cities in the new urban world: A comparative cluster dynamics analysis through machine learning," Sustain. Cities Soc., vol. 66, Art. no. 102665, 2021, doi: 10.1016/j.scs.2020.102665. [Google Scholar]
- B. Alhayani, H. J. Mohammed, I. Z. Chaloob, and J. S. Ahmed, "Effectiveness of artificial intelligence techniques against cyber security risks apply of IT industry," Mater. Today Proc., vol. 53, pp. 1–6, 2021, doi: 10.1016/j.matpr.2021.02.531. [Google Scholar]
- T. D. Wagner, K. Mahbub, E. Palomar, and A. E. Abdallah, "Cyber threat intelligence sharing: Survey and research directions," Comput. Secur., vol. 87, Art. no. 101589, 2019, doi: 10.1016/j.cose.2019.101589. [Google Scholar]
- J. Engelbert, L. van Zoonen, and F. Hirzalla, "Excluding citizens from the European smart city: The discourse practices of pursuing and granting smartness," Technol. Forecast. Soc. Change, vol. 142, pp. 347–353, 2019, doi: 10.1016/j.techfore.2018.09.017. [Google Scholar]
- S. I. Pérez and R. Criado, "Increasing the effectiveness of network intrusion detection systems (NIDSs) by using multiplex networks and visibility graphs," Mathematics, vol. 11, no. 1, Art. no. 107, 2022. [Google Scholar]
- I. H. Sarker, H. Janicke, M. A. Ferrag, and A. Abuadbba, "Multi-aspect rule-based AI: Methods, taxonomy, challenges, and directions toward automation, intelligence, and transparent cybersecurity modeling for critical infrastructures," Internet of Things, vol. 25, Art. no. 101110, Apr. 2024. [Google Scholar]
- T. Sasi, A. H. Lashkari, R. Lu, P. Xiong, and S. Iqbal, "A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms, and challenges," J. Inf. Intell., vol. 2, no. 6, pp. 455–513, Nov. 2024. [Google Scholar]
- S. M. Mousavi and M. St-Hilaire, "Early detection of DDoS attacks against SDN controllers," in Proc. Int. Conf. Comput., Netw. Commun. (ICNC), Garden Grove, CA, USA, Feb. 2015, pp. 77–81. [Google Scholar]
- V. R. S. Dora and V. N. Lakshmi, "Optimal feature selection with CNN-feature learning for DDoS attack detection using meta-heuristic-based LSTM," Int. J. Intell. Robot. Appl., vol. 6, no. 3, pp. 323–349, 2022. [Google Scholar]
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