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While it’s true that AI has enabled the automation of many RCM tasks, the promise of fully autonomous systems remains unfulfilled. Building a robust data foundation is critical, as the underlying data model with proper metadata, data quality, and governance is key to enabling AI to achieve peak efficiencies.
TL;DR: In many machine-learning projects, the model has to frequently be retrained to adapt to changing data or to personalize it. Continuallearning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. What is continuallearning?
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This is done on the features that security vendors might sign, starting from hardcoded strings, IP/domain names of C&C servers, registry keys, file paths, metadata, or even mutexes, certificates, offsets, as well as file extensions that are correlated to the encrypted files by ransomware.
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