Imam Abdulrahman Bin Faisal University

University of Dammam

Abdullah Saeed Almurayh

Alaa Yahya Alahmadi

An anomaly detection system includes multiple sensors fixed within a user-selected environment to measure environmental conditions and wirelessly transmit data corresponding to the environmental conditions to a main device. The main device includes circuitry encoded with instructions from a trained machine learning model to identify one or more anomalies from the data received from the sensors. The main device transmits data related to the anomalies and the environmental conditions to a cloud storage via a plurality of wired and wireless connections. The cloud storage wirelessly relays the data to a user device. The user device, based on user's feedback, relays a feedback signal, via the cloud storage, to the trained machine learning model, where the feedback signal is incorporated into the trained machine learning model for the purpose of retraining.

System and method for anomaly detection

A quiet zone can be implemented using a quiet zone transceiver. The quiet zone can have a predetermined perimeter created by the quiet zone transceiver. A smart mobile device can cause an initial alert setting of the smart mobile device to automatically change to a silent alert setting when the smart mobile device is moved into the quiet zone. Additionally, when the smart mobile device is moved out of the quiet zone, the initial alert setting can be restored. The smart mobile device can also be used as the quiet zone transceiver to create the quiet zone if the user of the smart mobile device wishes to create an area in which disturbances such as loud alert settings are limited.

Method and device for implementing a quiet zone

An anomaly detection system includes appliances connected to a network and circuitry configured to receive statuses of the appliances. Patterns of use are determined for the appliances including time periods in which the appliances are most likely to be used. The circuitry is also configured to determine that anomalous events have occurred when an amount of deviation from a normal baseline status for the appliances is greater than a predetermined threshold. Cyber-attack events are detected based on changes to network configuration settings of the appliances. Alerts are output to an external device based on the one or more anomalous events and/or cyber-attack events related to the appliances. The patterns of use are updated based on the status of the appliances and a response from the external device related to the anomalous events or cyber-attack events.

Growing community of inventors

Dammam, Saudi Arabia

Abdullah Saeed Almurayh