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IoT based Hydroponic System Using Deep Learning

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ML helps IoT security teams make intelligent predictions and responses based on previous behavior. In the case of known vulnerabilities and attacks, such as distributed denial of service, it compares current network behavior with behavior patterns from attack examples and takes protective action.

Services, such as AWS IoT Device Defender, Extreme Networks solutions or Microsoft's Azure Security Center for IoT, offer ML capabilities for IoT security, including device-level anomaly detection and automated threat response.

In the Microsoft's Windows Defender example, client-side and cloud-based ML systems automatically compare current network use against 30 security protection models in parallel. Some of those models use millions of factors to determine what's positive or negative behavior for

known attacks, IoT based Hydroponic System Using Deep Learning

To protect against unknown vulnerabilities and zero-day attacks, ML models monitor IoT devices and network activity to detect behavior that's out of the ordinary in real time and take protective measures immediately. Many ML systems automatically update daily to keep pace with the changing threat landscape, which makes ML ideal for protecting complex networks. It instantly reviews the large digital footprint of an IoT fleet and compares the fleet's behavior with known threats and historical behavior. Only a network using ML systems can act this quickly to spot threats before they break into the main corporate network via IoT devices.

 

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