As cyber threats increasingly target real-time communications and grow ever more sophisticated, traditional security measures are no longer sufficient. The UM-Labs AI Large Event Model (LEM) introduces a revolutionary approach to cybersecurity for real-time traffic by leveraging AI-powered threat detection, predictive intelligence, and automated response mechanisms. With over a decade’s worth of cyber-attack data contained in SNITCH Event Database, the model has the historical data to help anticipate future threats and thus offers the potential to deploy pre-emptive defences. The scalable and adaptable architecture ensures that security measures can be effectively implemented across diverse network environments, providing a robust and dynamic cybersecurity solution. By harnessing AI-driven analytics, cross-domain correlation, and automated threat mitigation, the UM-Labs LEM sets a new standard in proactive cybersecurity, enabling organisations to stay ahead of evolving threats and secure real-time communications more effectively than ever before.

Large Event Model, An Alternative AI Model for Analysing Attacks on Structured Network Protocols | AI Conference London 2025

SNITCH Attack Intelligence   +++  69,392 attacks from 91.121.218.38, location Belgium    +++  23,048 attacks from 185.243.5.99, location United States    +++  3,861 attacks from 173.231.185.164, location United States    +++  3,027 attacks from 23.94.253.178, location United States    +++  New attack source, Japan, 45.131.155.254     +++  New attack source, United States, 207.167.67.22     +++  New attack source, Hong Kong, 199.45.155.87     +++  New attack source, United States, 206.168.35.89  
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