Securing IoT networks is a crucial concern for businesses that rely on connected devices and networks. One key area of focus is the detection and prevention of intrusion attempts.
By implementing an IoT Intrusion Detection System, businesses can identify and respond to potential security threats in real-time. The system continuously monitors data from connected devices and networks, analyzing it for anomalies and suspicious activity that may indicate an intrusion attempt.
A well-trained ML model can detect patterns and anomalies in the data, and accurately identify intrusion attempts and respond to them promptly. It can also iteratively improve the accuracy and efficiency of intrusion detection and to automatically respond to detected threats. The models developed by RapidCanvas deal with the perennial trade-off between high accuracy, explainability, and actionable alerts by reducing false positive rates. Now, businesses can ensure the security of their IoT networks and protect against intrusion attempts with confidence.
* Values are approximates arrived at based on earlier experience and/or existing literature. Contact us to find out how you can measure the ROI on this solution for your business