Using machine learning to protect the proliferating landscape of IoT and edge devices will ultimately rely on a distributed network model that applies intelligence at different tiers, based on cost, bandwidth and availability.
Previously, computing power was centralized in the cloud or an on-premises data center. But many enterprise tasks require a decentralized model, where capabilities are brought closer to the devices and users that need these resources.
This need for low latency, data-rich digital capabilities is moving compute to the edge of the network. Computing power is distributed at the edge, fueling growth in data-driven intelligence among burgeoning numbers of Internet of Things (IoT) devices.
One downside to the rising popularity of edge computing is increasing infrastructure complexity. Protecting this extended infrastructure may ultimately depend on machine learning (ML) technologies to automate threat detection and response.
In this report, we explore the capabilities of machine learning for cybersecurity tasks.
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