Integrating AI-Based Anomaly Detection with MPK-Isolated Microservices for Proactive Security in Optical Networks

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.

Publication Details

Journal
PhilPapers (PhilPapers Foundation)
Published
2025-01-01
Primary Topic
Prenatal Screening and Diagnostics
Type
article
Field-Weighted Citation Impact
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article

Integrating AI-Based Anomaly Detection with MPK-Isolated Microservices for Proactive Security in Optical Networks

PhilPapers (PhilPapers Foundation)
Prenatal Screening and Diagnostics
article

Integrating AI-Based Anomaly Detection with MPK-Isolated Microservices for Proactive Security in Optical Networks

article en

Abstract

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.

PhilPapers (PhilPapers Foundation)
Industry, innovation and infrastructure
Openalex Percentile: Top 35%
Prenatal Screening and Diagnostics
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Integrating AI-Based Anomaly Detection with MPK-Isolated Microservices for Proactive Security in Optical Networks · PhilPapers (PhilPapers Foundation) (2025) | TGRS Research Map | TGRS