Condition monitoring via energy consumption audit in electrical devices and electrical waveform audit in power networks

1. A system, comprising:

an electrical device coupled to a network;

an energy meter coupled to the electrical device, the energy meter configured to monitor an energy profile of the electrical device, the energy meter isolated from an internet of things (IoT) system including the electrical device; and

at least one application executable in a computing device coupled to the energy meter, wherein, when executed, the at least one application causes the computing device to at least:

analyze energy profile data of the electrical device, the energy profile data being received from the energy meter based on the monitoring of the energy profile;

monitor one or more conditions of the electrical device based on the energy profile data;

detect an anomaly in the electrical device based on a change in the one or more conditions;

determine a type of anomaly based at least in part on a trained classification model; and

diagnose a root cause of the anomaly based at least in part on one or more characteristics in the energy profile data and the type of anomaly, the root cause comprising at least one of a threat source or a malfunctioning component of the electrical device.

2. The system of claim 1 , wherein the energy profile data includes at least one of voltage measurements, current measurements, or power measurements.

3. The system of claim 1 , wherein analyzing the energy profile data comprises comparing the energy profile data with one or more energy profile models.

4. The system of claim 3 , wherein, when executed, the at least one application further causes the at least one computing device to at least identify a type of attack associated with the anomaly based at least in part one or more characteristics in the energy profile data.

5. The system of claim 1 , wherein detection of the anomaly is based at least in part on Finite State Machine (FSM) reconstruction.

6. The system of claim 1 , wherein, when executed, the at least one application further causes the at least one computing device to at least reconstruct an energy profile model based at least in part on the energy profile data.

7. The system of claim 1 , wherein detection of the anomaly is based at least in part on cross-correlation.

8. The system of claim 1 , wherein the electrical device comprises an internet of things (IoT) device or an electrical appliance.

9. The system of claim 1 , wherein analyzing the energy profile data further comprises analyzing waveform data associated with the electrical device.

10. The system of claim 1 , wherein the anomaly is a result of a cyber-attack, a physical attack, a hardware malfunction, or a software malfunction.

11. A method for detecting an anomaly in an electrical device, comprising:

monitoring, via an energy meter coupled to the electrical device, energy profile data of the electrical device, wherein the energy meter is isolated from an internet of things (IoT) system including the electrical device;

comparing the energy profile data with one or more energy profile models, the one or more energy profile models based at least in part on expected behavior of the electrical device; and

detecting an anomaly associated with the electrical device based at least in response to the comparing of the energy profile data with the one or more energy profile models; and

determining a type of anomaly based at least in part on the energy profile data and a classification model.

12. The method of claim 11 , wherein the energy profile data comprises at least one or more of voltage data, current data, or power data.

13. The method of claim 11 , further comprising identifying a type of attack associated with the anomaly based at least in part on one or more characteristics in the energy profile data.

14. The method of claim 11 , wherein detection of the anomaly is based at least in part on cross-correlation.

15. The method of claim 11 , wherein detection of the anomaly is based at least in part on Finite State Machine (FSM) reconstruction.

16. The method of claim 11 , further comprising notifying an entity of the detection of the anomaly.

17. The method of claim 11 , wherein the anomaly is a result of a cyber-attack, a physical attack, hardware malfunction, or software malfunction.

18. A system, comprising:

a power network comprising a plurality of electrical components; and

an electrical waveform auditory device coupled to the power network, the electrical waveform auditory device isolated from the plurality of electrical components, the electrical waveform auditory device being configured to:

receive electrical waveform data associated with the power network;

monitor one or more conditions of the plurality of electrical components in the power network based on the electrical waveform data;

detect an anomaly in at least one of the electrical components based at least in part on a change in the one or more conditions; and

diagnose a root cause of the anomaly based at least in part on one or more characteristics in the electrical waveform data and a type of anomaly, the root cause comprising at least one of a threat source or a malfunctioning component of the plurality of electrical components.

19. The system of claim 18 , wherein the power network comprises at least one of: an electrical vehicle power network, a home power network, a building power network, a manufacturing system power network, a microgrid, a power distribution network, a power transmission network, or a power generating network.

20. The system of claim 18 , wherein the type of anomaly is at least one of: a cyber threat, a physical threat, a hardware malfunction, or a software malfunction.