Off-the-Shelf Solutions
Are Limited
Most anomaly detection algorithms perform heuristics without provable guarantees performance in either running time and especially quality of anomaly detection.
Many existing anomaly detection algorithms have provable guarantees that are totally impractical, usually in terms of running time.
Most anomaly detection algorithms perform heuristics without provable guarantees performance in either running time and especially quality of anomaly detection.
Accurate Anomaly Detection At Scale
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.
Higher Detection Rates with Lower False Alarms
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.
The “magic” of ShieldIOT Coreset technology is that it delivers accurate analytics at scale in a fraction of the time required by alternative off-the-shelf products. In practice, Coreset does not directly solve the anomaly detection problem, instead it tries to solve the data problem by using much smaller datasets.
Coresets are mathematical constructs that compress the data in real-time streaming mode, from 1 million to 20 data points (n to log(n)), with almost zero loss of energy. By running Coresets on reduced datasets before applying existing AI/ML anomaly detection algorithms, Coreset-based solutions can deliver optimal results in minutes compared to hours or days. These results include significant lower false alarms levels (false positives) as well as improved detection rates (true positives).
Existing non-Coreset based solutions, need to perform various heuristics to enable analysis of mass scale data sets. This method results in very high levels of false alarms (in some cases exceeding 95% false positives).
Research Results
Based on 15 Years Academic Research
Based on 15 Years Academic Research
Published by Dan Feldman and Yair Marom
Published by Dan Feldman and Leonard J. Schulman.
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.
Shield-IoT is an IoT cyber security and analytics software solution provider, enabling IoT service and solution providers to monitor and secure mass-scale B2B IoT/IIoT networks, reduce operational costs and generate new revenue streams. Based on over 15 years of research (MIT) and 80 academic papers, Shield-IoT patented technology delivers the world’s first coreset-AI anomaly detection solution to enable accurate analytics at mass scale.
For more information about Shield-IoT solutions, send us an email and a Shield-IoT expert will contact you.
Cookie | Duration | Description |
---|---|---|
cookielawinfo-checkbox-analytics | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics". |
cookielawinfo-checkbox-functional | 11 months | The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". |
cookielawinfo-checkbox-others | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. |
cookielawinfo-checkbox-performance | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance". |
viewed_cookie_policy | 11 months | The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data. |