Autonomous shift identification in pervasive data flows within decentralized networks
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Abstract
Decentralized networks, with their distributed control and peer-to-peer communication, are generating vast amounts of data streams. Analyzing these "pervasive data flows" is crucial, but automatically detecting significant shifts within them remains a challenge. Traditional methods using predefined thresholds struggle to adapt to the dynamic nature of these networks. This research addresses this by proposing a novel framework for autonomous shift identification. Our machine learning-based approach automatically detects significant changes in data streams, eliminating the need for manual configuration and adapting to evolving network behavior. We evaluate the framework on datasets simulating real-world scenarios, using metrics like accuracy in identifying shifts. By enabling autonomous shift identification, this research offers a more robust and responsive approach to decentralized network monitoring. This has significant implications for applications like blockchain technology, peer-to-peer communication systems, and the Internet of Things (IoT), ultimately enhancing their security and management.
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