An integrated intent-driven verification and distributed monitoring framework strengthens network infrastructure security by uniting real-time traffic analysis, machine learning–based threat detection, and automated response. The system improves validation accuracy, scalability, and incident response speed, enabling resilient security operations across complex, high-density network environments.
-- Network infrastructure faces persistent challenges in validation accuracy and real-time security monitoring as distributed systems grow in scale and complexity. Recent research published in the Journal of Computer, Signal, and System Research presents an integrated framework that unites verification architectures with intelligent threat detection models. The proposed approach enhances system reliability by combining intent-based validation with adaptive security monitoring, achieving dynamic coordination between configuration assurance and threat response within high-density network environments. Building upon this foundation, the study further details the design of a distributed monitoring architecture supporting intelligent security operations.
The security architecture incorporates distributed monitoring and deep packet inspection technologies in conjunction with machine learning algorithms to support multi-layer threat analysis. By improving data collection efficiency and real-time analytic throughput, the framework establishes modular components that perform traffic capture, feature extraction, and automated response orchestration. Parallel processing mechanisms alleviate detection bottlenecks, maintaining analysis precision across diverse attack patterns. Experimental results demonstrate millisecond-level response latency and high threat identification accuracy across distributed deployment scenarios, confirming the system’s scalability and robustness. These experimental results provide the empirical foundation for the system’s implementation and real-world deployment strategies.
From an implementation perspective, the study focuses on building a computer network security monitoring system with a modular architecture, integrating data collection, threat analysis, automated response, and visualization components. The system employs deep packet inspection technology to capture network traffic in real time and extract key information such as communication protocols, timestamps, and address data. Machine learning–based analysis modules identify abnormal patterns and generate threat reports, while automated response mechanisms apply appropriate security policies, including isolation and traffic control, according to detected risks. Through decentralized and parallel processing structures, the framework achieves efficient coordination among modules, ensuring rapid detection, real-time control, and continuous optimization of security operations across complex network environments. The implementation outcomes form the basis for subsequent developments in visualization, risk assessment, and adaptive policy generation.
Expanding upon the system’s foundational implementation, recent work further enhances its comprehensive security monitoring capabilities. The network security monitoring system implements a modular architecture encompassing data collection, preprocessing, threat analysis, and automated response mechanisms. The framework integrates visualization dashboards presenting security event analysis, log review capabilities, and risk assessment reports. Implementation features include traffic anomaly detection using clustering algorithms, protocol behavior analysis identifying covert attacks, and automated policy generation based on threat intelligence, enabling rapid incident response across enterprise network environments.
Contributing to this research is Yihong Zou, holding a Master's degree in Computer Science from Carnegie Mellon University and dual Bachelor's degrees in Computer Science from the University of Michigan and Electrical and Computer Engineering from Shanghai Jiao Tong University. Professional experience at Amazon from 2024 and Zoom Video Communications from 2021 to 2024 focused on distributed system architectures, achieving scalable verification and real-time event processing. His published research in the Journal of Computer, Signal, and System Research has been recognized for demonstrating the ability to apply advanced distributed system concepts to real-world engineering practice.
This body of work represents significant contributions spanning network infrastructure verification and cybersecurity operations. By combining scalable verification methodologies with intelligent threat analysis and distributed monitoring systems, this research advances the reliability of network change validation and enhances automation and security incident response across complex computing environments.
Contact Info:
Name: Yihong Zou
Email: Send Email
Organization: Yihong Zou
Website: https://scholar.google.co.uk/citations?user=gchjjhoAAAAJ
Release ID: 89176320
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