Open Access
Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
|
|
---|---|---|
Article Number | 01047 | |
Number of page(s) | 10 | |
DOI | https://doi.org/10.1051/epjconf/202532801047 | |
Published online | 18 June 2025 |
- Scarfone, K., Meli, P.: Guide to intrusion detection and prevention systems (IDPS). NIST Special Publication 800(94), 1-127 (2022) [Google Scholar]
- IBM: Cost of a Data Breach Report 2023. https://www.ibm.com/reports/data-breach [Google Scholar]
- Danso, P.K., Neto, E.C.P., Dadkhah, S., Zohourian, A., Molyneaux, H., Ghorbani, A.A.: Ensemble-based intrusion detection for internet of things devices. In: 2022 IEEE 19th Int. Conf. on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), pp. 034-039. IEEE, Marietta, GA, USA (2022). https://doi.org/10.1109/HONET56683.2022.10019140 [CrossRef] [Google Scholar]
- Akinade, A.O., Adepoju, P.A., Ige, A.B., Afolabi, A.I.: Cloud security challenges and solutions: A review of current best practices. Int. J. Multidiscip. Res. Growth Eval. 6(1) (2025) [Google Scholar]
- Hammi, B., Zeadally, S.: Software supply-chain security: Issues and countermeasures. Comput. 56(7), 54-66 (2023). https://doi.org/10.1109/MC.2023.3273491 [Google Scholar]
- El-Kassabi, H.T., Serhani, M.A., Masud, M.M., et al.: Deep learning approach to security enforcement in cloud workflow orchestration. J. Cloud Comput. 12, 10 (2023). https://doi.org/10.1186/s13677-022-00387-2 [CrossRef] [Google Scholar]
- Perez, F., et al.: Privacy-preserving deep learning using deformable operators. In: Proc. IEEE ICASSP, pp. 5980-5984 (2024) [Google Scholar]
- World Economic Forum: Global Cybersecurity Outlook 2025. https://reports.weforum.org/docs/WEF_Global_Cybersecurity_Outlook_2025.pdf [Google Scholar]
- Falade, P.V. : Decoding the threat landscape: ChatGPT, FraudGPT, and WormGPT in social engineering attacks. arXiv preprint arXiv:2310.05595 (2023) [Google Scholar]
- EmBroker: Top 16 Cybersecurity Threats in 2025. https://www.embroker.com/blog/top-cybersecurity-threats/ [Google Scholar]
- Fortinet: New and emerging cybersecurity threats and attacker tactics. Fortinet Blog (2024). https://www.fortinet.com/blog/ciso-collective/emerging-cybersecurity-threats-and-attack-tactics [Google Scholar]
- Greenberg, A.: Brass Typhoon: The Chinese hacking group lurking in the shadows. WIRED (Apr. 2025). https://www.wired.com/story/brass-typhoon-china-cyberspies/ [Google Scholar]
- Greenberg, A.: CyberAv3ngers: The Iranian saboteurs hacking water and gas systems worldwide. WIRED (Apr. 2025). https://www.wired.com/story/cyberav3ngers-iran-hacking-water-and-gas-industrial-systems/ [Google Scholar]
- Fortra: 2025 State of Cybersecurity Survey Results. https://www.fortra.com/resources/guides/fortra-state-cybersecurity-survey-results [Google Scholar]
- KPMG: KPMG 2024 Cybersecurity Survey. https://kpmg.com/us/en/media/news/2024-cybersecurity-survey.html [Google Scholar]
- Wikipedia: Internet security awareness. https://en.wikipedia.org/wiki/Internet_security_awareness [Google Scholar]
- Jullian, O., Otero, B., Rodriguez, E., et al.: Deep-learning based detection for cyber-attacks in IoT networks: A distributed attack detection framework. J. Netw. Syst. Manag. 31, 33 (2023). https://doi.org/10.1007/s10922-023-09722-7 [CrossRef] [Google Scholar]
- Wang, W., Harrou, F., Bouyeddou, B., Senouci, S.-M., Sun, Y.: Cyber-attacks detection in industrial systems using artificial intelligence-driven methods. Int. J. Crit. Infrastruct. Prot. 38, 100542 (2022). https://doi.org/10.1016/j.ijcip.2022.100542 [CrossRef] [Google Scholar]
- Jyothi, K.K., Borra, S.R., Srilakshmi, K., et al.: A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm. Sci. Rep. 14, 5590 (2024). https://doi.org/10.1038/s41598-024-55098-2 [CrossRef] [Google Scholar]
- Bhalme, A., Pawar, A., Borkar, P., Shriram, P.: Cyber attack detection and implementation of prevention methods for web application. In: 2022 IEEE Bombay Section Signature Conference (IBSSC), pp. 1-6. IEEE, Mumbai, India (2022). https://doi.org/10.1109/IBSSC56953.2022.10037431 [Google Scholar]
- Nandhini, S., Rajeswari, A., Shanker, N.R.: Cyber attack detection in IoT-WSN devices with threat intelligence using hidden and connected layer based architectures. J. Cloud Comput. 13, 159 (2024). https://doi.org/10.1186/s13677-024-00722-9 [CrossRef] [Google Scholar]
- Tomar, K., Bisht, K., Joshi, K., Katarya, R. : Cyber attack detection in IoT using deep learning techniques. In: 2023 6th Int. Conf. on Inf. Syst. Comput. Netw. (ISCON), pp. 1-6. IEEE, Mathura, India (2023). https://doi.org/10.1109/ISCON57294.2023.10111990 [Google Scholar]
- Coyac-Torres, J.E., Sidorov, G., Aguirre-Anaya, E., Hernandez-Oregön, G.: Cyberattack detection in social network messages based on convolutional neural networks and NLP techniques. Mach. Learn. Knowl. Extr. 5(3), 1132-1148 (2023). https://doi.org/10.3390/make5030058 [CrossRef] [Google Scholar]
- Xu, S., Qian, Y., Hu, R.Q. : Data-driven edge intelligence for robust network anomaly detection. IEEE Trans. Netw. Sci. Eng. 7(3), 1481-1492 (2020). https://doi.org/10.1109/TNSE.2019.2936466 [CrossRef] [Google Scholar]
- Park, M., Lee, H., Kim, Y., Kim, K., Shin, D.: Design and implementation of multicyber range for cyber training and testing. Appl. Sci. 12(24), 12546 (2022). https://doi.org/10.3390/app122412546 [CrossRef] [Google Scholar]
- Alshehri, A., Khan, N., Alowayr, A., Alghamdi, M.Y.: Cyberattack detection framework using machine learning and user behavior analytics. Comput. Syst. Sci. Eng. 44(2), 1679-1689 (2023). https://doi.org/10.32604/csse.2023.026526 [CrossRef] [Google Scholar]
- Delplace, A., Hermoso, S., Anandita, K.: Cyber attack detection thanks to machine learning algorithms. arXiv preprint arXiv:2001.06309 (2020). https://arxiv.org/abs/2001.06309 [Google Scholar]
- Bahadoripour, S., MacDonald, E., Karimipour, H.: A deep multi-modal cyber-attack detection in industrial control systems. arXiv preprint arXiv:2304.01440 (2023). https://arxiv.org/abs/2304.01440 [Google Scholar]
- Ghonge, M.M., Pramanik, S., Mangrulkar, R.S., Le, D.-N.: Cybersecurity and digital forensics: Challenges and future trends. John Wiley & Sons, New York (2022) [Google Scholar]
- Naser, M., Ali, H., Al-Jumeily, O. : Hybrid cyber-security model for attacks detection based on deep and machine learning. Int. J. Online Biomed. Eng. (iJOE) 18(11), 1730 (2022). https://doi.org/10.3991/ijoe.v18i11.33563 [Google Scholar]
- Babu, D.R.K., Packialatha, A.: Cyber-attack detection and mitigation process under big data consideration: Improved recursive feature elimination-based feature selection. J. Inf. Knowl. Manag. 23(6), 2450079 (2024). https://doi.org/10.1142/S0219649224500795 [CrossRef] [Google Scholar]
- Mahdi, Z.S., Zaki, R.M., Alzubaidi, L.: Advanced hybrid techniques for cyberattack detection and defense in IoT networks. Secur. Priv. 8, e471 (2025). https://doi.org/10.1002/spy2.471 [CrossRef] [Google Scholar]
- Jianping, W., Guangqiu, Q., Chunming, W., et al.: Federated learning for network attack detection using attention-based graph neural networks. Sci. Rep. 14, 19088 (2024). https://doi.org/10.1038/s41598-024-70032-2 [CrossRef] [Google Scholar]
- Abbas, S., Bouazzi, I., Ojo, S., Al Hejaili, A., Sampedro, G.A., Almadhor, A., Gregus, M.: Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks. PeerJ Comput. Sci. 10, e1793 (2024). https://doi.org/10.7717/peerj-cs.1793 [CrossRef] [Google Scholar]
- Gopalakrishnan, T., Ruby, D., Al-Turjman, F., Gupta, D., Pustokhina, I.V., Pustokhin, D.A., Shankar, K.: Deep learning enabled data offloading with cyber attack detection model in mobile edge computing systems. IEEE Access 8, 185938-185949 (2020). https://doi.org/10.1109/ACCESS.2020.3030726 [CrossRef] [Google Scholar]
- Ghafoori, M.S., Soltani, J.: Designing a robust cyber-attack detection and identification algorithm for DC microgrids based on Kalman filter with unknown input observer. IET Gener. Transm. Distrib. 16, 3230-3244 (2022). https://doi.org/10.1049/gtd2.12517 [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.