AI-based coresets is a powerful technique which enables the use of smaller sets of data instead of larger ones without compromising the quality of the output. This approach is based on highly efficient set of algorithms that reduce the data input on one hand, and execute anomaly detection routines on the reduced dataset (coreset), on the other hand. This technology has been applied to fixed big datasets, distributed data or to streaming data, which is highly relevant feature when we aim at network traffic anomaly detection.
Stastical research was undertaken to compare ShieldIOT AI error rate (false positive) versus RANSAC, a state of the art anomaly detection algorithm (data: security camera, 114 features per data sample). The results show that even for a very small number of samples (2500) Shield-IoT exhibited a superior accuracy of more than X5 better, and that the accuracy-level is almost identical to naively running on the entire data set (which is of course not feasible in large IoT networks / memory-sensitive edge devices). The proven ability to reduce the data into coreset, without losing important and relevant information is at the heart of the innovation of this AI-based anomaly detection solution. Since the coreset is small compared to the original big data, it is possible to execute and run more anomaly detection algorithms in a given time. Thus, the effective data reduction enables significantly better execution time, allowing faster detection and response time to any anomaly over network traffic.
ShieldI-IoT is 20x more accurate compared to other anomaly detection vendors
ShieldI-IoT reduces detection time from one week to only one minute
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Solutions Across Mass-scale IoT Networks
Shieild-IoT software solutions are deployed across a wide range of industry use cases and applications. Our purpose-built IoT security management solution provides continuous threat mitigation with no changes to your network.