17932494. CORROBORATING DEVICE-DETECTED ANOMALOUS BEHAVIOR simplified abstract (International Business Machines Corporation)
Contents
- 1 CORROBORATING DEVICE-DETECTED ANOMALOUS BEHAVIOR
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CORROBORATING DEVICE-DETECTED ANOMALOUS BEHAVIOR - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Prior Art
- 1.10 Original Abstract Submitted
CORROBORATING DEVICE-DETECTED ANOMALOUS BEHAVIOR
Organization Name
International Business Machines Corporation
Inventor(s)
Kevin W. Brew of Niskayuna NY (US)
Michael S. Gordon of Chappaqua NY (US)
Mattias Fitzpatrick of Mount Kisco NY (US)
Brian Paul Gaucher of Brookfield NY (US)
CORROBORATING DEVICE-DETECTED ANOMALOUS BEHAVIOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 17932494 titled 'CORROBORATING DEVICE-DETECTED ANOMALOUS BEHAVIOR
Simplified Explanation
The patent application describes techniques for corroborating anomalous behavior in an IoT mesh network.
- Training devices in the IoT mesh network can independently identify occurrences of anomalous behavior in the physical environment.
- Event data from devices in the network is collected to report anomalous behavior.
- The occurrences of anomalous behavior are corroborated to determine if they meet a reporting threshold for providing notice of an anomalous event.
- Notifications are generated regarding the anomalous event.
Potential Applications
This technology could be applied in various industries such as security, healthcare, and manufacturing to detect and respond to anomalous behavior in real-time.
Problems Solved
This technology helps in identifying and addressing anomalous behavior quickly, which can prevent security breaches, safety hazards, and operational disruptions.
Benefits
The benefits of this technology include improved security, safety, and operational efficiency by detecting and responding to anomalous behavior promptly.
Potential Commercial Applications
Potential commercial applications of this technology include security systems, smart buildings, and industrial automation for enhanced monitoring and response to anomalous events.
Prior Art
One possible prior art for this technology could be existing anomaly detection systems used in various industries to monitor and alert for unusual behavior or events.
Unanswered Questions
This technology could potentially raise privacy concerns regarding the monitoring and collection of data related to anomalous behavior. Implementing strict data protection measures, obtaining consent from individuals being monitored, and ensuring data anonymization could help address these concerns.
How scalable is this technology for large-scale IoT deployments across different environments?
The scalability of this technology for large-scale IoT deployments in diverse environments could be a challenge. Ensuring interoperability with different devices, optimizing data processing and storage capabilities, and managing network bandwidth effectively are key factors to consider for scalability.
Original Abstract Submitted
Described are techniques for corroborating anomalous behavior. The techniques include training devices included in an Internet of Things (IoT) mesh network to independently identify occurrences of anomalous behavior in a proximate physical environment. The techniques further include receiving event data from at least a portion of the devices in the IoT mesh network corresponding to a time window, where the event data reports occurrences of at least one type of anomalous behavior. The techniques further include corroborating the at least one type of anomalous behavior to determine that the occurrences of the at least one type of anomalous behavior indicate an anomalous event that meets a reporting threshold for providing notice of the anomalous event. The techniques further include generating a notification regarding the anomalous event.
