Computer Network Management and Security in the Age of Artificial Intelligence
DOI:
https://doi.org/10.5281/zenodo.18213751Keywords:
Big data, Artificial intelligence, Network security, Network managementAbstract
Due to the increasing amount of data, the interaction of artificial intelligence with computer networks has led to many innovations. The flow of large amounts of data over networks has emerged as an innovation that moves society forward. Computer network technology, an important method for transmitting and processing information in modern society, is undergoing tremendous transformations. The concept of big data brings with it unprecedented challenges and opportunities for network architecture, protocols, and even security measures. At the same time, the rapid advancement of artificial intelligence technologies further strengthens this phenomenon. Every stage of network security and management has begun to use the efficiency and intelligent solutions offered by artificial intelligence. This synergy is taking network technology beyond traditional boundaries and moving towards a future that promises greater intelligence and efficiency.
References
Altunay, H. C. (2024). Analysis of cyber attacks using honeypot. Black Sea Journal of Engineering and Science, 7(5), 954–959. https://doi.org/10.52704/bsscience.1481075
Altunay, H. C. (2025, April 24–25). Dijital ekonomi ve blok zinciri uygulamaları [Bildiri sunumu]. 5. Uluslararası Yapay Zeka ve Veri Bilimi Kongresi, İzmir, Türkiye.
Altunay, H. C., & Albayrak, Z. (2024). SMS spam detection system based on deep learning architectures for Turkish and English messages. Applied Sciences, 14(24), 11804. https://doi.org/10.3390/app142411804
Balbix. (2021). Balbix BreachControl. https://www.balbix.com/product-overview/
Balduzzi, M., & Maggi, F. (2017, September 14). DefPloreX: A machine-learning toolkit for large-scale eCrime forensics. Trend Micro. https://blog.trendmicro.com/trendlabs-security-intelligence/defplorex-machine-learning-toolkit-large-scale-ecrime-forensics/
Calix, R. A., Singh, S. B., Chen, T., Zhang, D., & Tu, M. (2020). Cyber security tool kit (CyberSecTK): A Python library for machine learning and cyber security. Information, 11(2), 100. https://doi.org/10.3390/info11020100
Demertzis, K., & Iliadis, L. (2015). A bio-inspired hybrid artificial intelligence framework for cyber security. In N. Daras & M. Rassias (Eds.), Computation, cryptography, and network security (pp. 161–193). Springer. https://doi.org/10.1007/978-3-319-18275-9_7
Effah, E. Q., Osei, E. O., Jnr, M. D., & Tetteh, A. (2024). Hybrid approach to classification of DDoS attacks on a computer network infrastructure. Asian Journal of Research in Computer Science, 17(4), 19–43. https://doi.org/10.9734/ajrcos/2024/v17i4427
FireEye. (2020). StringSifter: A machine learning tool that ranks strings based on their relevance for malware analysis [Computer software]. GitHub. https://github.com/mandiant/stringsifter
IBM. (2021). QRadar Advisor with Watson. https://www.ibm.com/in-en/products/cognitive-security-analytics
Lacava, A., Bonati, L., Mohamadi, N., Gangula, R., Kaltenberger, F., Johari, P., & Melodia, T. (2025). dApps: Enabling real-time AI-based Open RAN control. Computer Networks, 254, 111342. https://doi.org/10.1016/j.comnet.2024.111342
Lin, Y. (2024). Application and challenges of computer networks in distance education. Computer Performance and Communication Systems, 8(1), 17–24.
Madrid, S. (2020). Juniper strengthens connected security portfolio with new risk-based access control capabilities and remote access VPN. Juniper Networks. https://blogs.juniper.net/en-us/security/juniper-strengthens-connected-security-portfolio-with-new-risk-based-access-control-capabilities-and-remote-access-vpn
Marino, D. L., Wickramasinghe, C. S., Rieger, C., & Manic, M. (2025). Self-supervised and interpretable anomaly detection using network transformers. IEEE Transactions on Industrial Informatics, 21(5), 4252–4261. https://doi.org/10.1109/TII.2024.3414920
Newman, L. H. (2018). AI can help cybersecurity—If it can fight through the hype. Wired. https://www.wired.com/story/ai-machine-learning-cybersecurity
Nowroozi, E., Haider, I., Taheri, R., & Conti, M. (2025). Federated learning under attack: Exposing vulnerabilities through data poisoning attacks in computer networks. IEEE Transactions on Network and Service Management, 22(1), 822–831. https://doi.org/10.1109/TNSM.2024.3444055
Qiao, W. (2025). Comprehensive framework for collaborative decision-making in evaluating computer network security using interval neutrosophic information. Neutrosophic Sets and Systems, 76, 520–537.
Sophos. (2020). Intercept X: Stop unknown threats. https://www.sophos.com/en-us/products/intercept-x
Symantec. (2018). Targeted attack analytics. Broadcom. https://docs.broadcom.com/doc/targeted-attack-analytics-en
Vectra AI. (2021). Cognito Platform: Network detection and response built on artificial intelligence. https://www.vectra.ai/products/cognito-platform
Yao, K., Pan, F., Liang, H., Zhang, X., Li, L., Song, L., & Lu, W. (2025). Shifting d‐band center: An overlooked factor in broadening electromagnetic wave absorption bandwidth. Advanced Functional Materials, 35(3), 2413639. https://doi.org/10.1002/adfm.202413639
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Black Sea Journal of Artificial Intelligence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.