Integrating AI-Based Anomaly Detection with MPK-Isolated Microservices for Proactive Security in Optical Networks
DOI:
https://doi.org/10.32628/CSEIT25112537Keywords:
AI-powered anomaly detection, Microservices, Multi-Protocol Kinematics, Optical network security, Real-time detection and responseAbstract
Our work dives into mixing AI-powered anomaly detection with microservices segregated by Multi-Protocol Kinematics (MPK), all meant to shore up security in optical networks. We hit a point where, generally speaking, traditional detection methods just couldn’t handle the vulnerabilities these networks face. Using a huge dataset of everyday traffic and those odd, unexpected spikes, we pieced together a system that speeds up real-time detection and response—often in ways that feel both innovative and, well, a bit off the beaten path. One standout is that this combo boosts how often we catch anomalies by nearly 30% over older techniques, and it slashes false alerts by about 25%; results like that really help make the whole operation more trustworthy. It’s key in places like healthcare too—where optical networks aren't just transferring data, they're safeguarding sensitive patient info. Keeping these data streams solid and secure builds trust in digital health systems and even bumps up overall patient safety. Plus, this approach might just serve as a rough blueprint for future security measures in other sectors that lean on optical networks, helping nudge our entire digital infrastructure towards being a bit more secure.
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A. A. H. D. Almowsawi, "Deep Guard-IoT: A Systematic Review of AI-Based Anomaly Detection Frameworks for Next-Generation IoT Security (2020-2024)," Wasit J. Pure Sci., 2024.
S. Wei, Z. Fan, G. Chen, E. Blasch, Y. Chen, and K. Pham, "TADAD: Trust AI-Based Decentralized Anomaly Detection for Urban Air Mobility Networks at Tactical Edges," in Proc. Int. Conf. Integr. Commun., Navig. Surveillance (ICNS), 2024.
S. Niranjana, K. Kumar, K. Malathy, C. K., and A. Y., "An Improved Intrusion Detection System for Social Security Using AI-Based Technique," in Proc. 15th Int. Conf. Comput. Commun. Netw. Technol. (ICCCNT), 2024.
A. Aluwala, "AI-Driven Anomaly Detection in Network Monitoring Techniques and Tools," J. Artif. Intell. Cloud Comput., 2024.
S. H. Rafique, A. Abdallah, N. S. Musa, and T. Murugan, "Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends," Sensors (Basel), 2024.
C. Maudoux and S. Boumerdassi, "DiNATrA: A Network Anomalies Detection Framework," in Proc. IEEE Int. Conf. Commun. (ICC), 2024.
M. A. Siddiqui, M. Kalra, and C. R. Krishna, "ADSBAN: Anomaly Detection System for Body Area Networks Utilizing IoT and Machine Learning," Concurrency Comput. Pract. Exp., 2024.
A. Ostroukh, N. G. Kuftinova, A. M. Borzenkov, A. A. Podberezkin, and I. A. Ostroukh, "Research on Using Deep Learning for Transport Demand Prediction," in Proc. Intell. Technol. Electron. Devices Veh. Road Transp. Complex (TIRVED), 2024.
A. Sebbar, O. Cherqi, K. Chougdali, and M. Boulmalf, "Real-Time Anomaly Detection in SDN Architecture Using Integrated SIEM and Machine Learning for Enhancing Network Security," in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2023.
R. Nair, "Unraveling the Decision-Making Process: Interpretable Deep Learning IDS for Transportation Network Security," J. Cybersecurity Inf. Manag., 2023.
K. C. Chen, L. Hanzo, C. Jiang, and et al., "Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks," IEEE, 2019.
A. Di Giglio, M. F. Prekratic, C. N. Da Silva, and et al., "Root Cause Analysis for Autonomous Optical Network Security Management," IEEE, 2022.
J. G. Munyua, S. T. Njenga, and G. M. Wambugu, "A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos," Asian Online J., 2021.
J. Ibrahim, "Architecture of a System for Recognizing Anomalies in Network Traffic Based on Entropy Analysis," Univ. Beograd, Electrotech. Fak., 2022.
A. Löf, "Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach," Univ. Waikato, 2013.
B. Adebisi, T. Baker, S. Belguith, and et al., "Network Traffic Analysis for Threats Detection in the Internet of Things," IEEE, 2020.
A. J. Slagell and W. Yurcik, "Sharing Computer Network Logs for Security and Privacy: A Motivation for New Methodologies of Anonymization," ArXiv, 2004.
R. Chacón, H. Posada, C. Ramonell, M. Jungmann, T. Hartmann, R. Khan, and R. Tomar, "Digital Twinning of Building Construction Processes: Case Study—A Reinforced Concrete Cast-in Structure," J. Build. Eng., 2024.
P. George, "Message from the Executive Chairman and Secretary," IEEE, 2023.
C. Serôdio, J. Cunha, G. Candela, S. Rodríguez, X. R. Sousa, and F. Branco, "The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges," Future Internet, 2023.
J. Jangid, “Secure Microservice Communication in Optical Networks,” Journal of Information Systems Engineering and Management, 2025, https://doi.org/10.52783/jisem.v10i21s.3455
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