Abstract: Any system with an interface may be attacked by a bad actor. If that interface is exposed to a network, the bad actor may launch a remote attack or cause other systems to attack the system. Many attacks exploit vulnerabilities that are unknown to the system operators (e.g., zero-day attacks). Power grid components, such as electricity meters, are increasingly networked and, therefore, increasingly attacked. By determining a pattern of behavior for a meter and then looking for a variation of the pattern, an attack may be identified. Once an attack is discovered, countermeasures may be launched to restore the system to normal operations, harden the system against future attack, and/or retaliate against the attacker.

Abstract: An Artificial Intelligence (AI) interface and engine is described that enables the monitoring and analysis of vehicle information to determine if the vehicle has had at least one of hardware and software maliciously changed, added, or removed. The AI interface may determine the presence of the maliciously changed, added, or removed hardware and/or software such as by receiving an emergency condition from at least one sensor that is in disagreement with another sensor.

Abstract: Methods, systems, and apparatuses for proactively protecting a computing network are disclosed. A proactive security mechanism is disclosed, among other things, with the ability to monitor a protected domain in real-time and safely identify inoculation procedures for responding to threats introduced to the protected domain via malware. The proactive security mechanism includes an Artificial Neural Network Interface (ANNI) configured to execute at least some features of the proactive security mechanism.

Abstract: An Artificial Neural Network Interface (ANNI) is disclosed along with use cases for the same. The ANNI utilizes one or more decision trees and/or probabilistic/combinatoric analysis to determine optimal responses to current conditions. The ANNI is also enabled to learn new conditions that are accepted as normal and, in response thereto, update the decision tree(s).

Abstract: A stem cell grid is disclosed. The stem cell grid includes the ability to incorporate characteristics of a stem cell into a network device. In the event that the network device fails or otherwise becomes unavailable for use by other network devices, the network device is automatically replicated within a virtualized environment and then the replica of the network device is used instead of the failed and/or unavailable network device.

Abstract: A learning framework and methods of machine learning are disclosed. Specifically, an Analytical Neural Network Intelligent Interface (ANNII) is disclosed that includes the ability to analyze incoming data in substantially real-time and determine whether or not the data is statistically anomalous data. Learning models can then be updated depending upon whether or not the data is determined to be statistically anomalous data or not.

Abstract: Methods, systems, and apparatuses for proactively protecting a computing network are disclosed. A proactive security mechanism is disclosed, among other things, with the ability to monitor a protected domain in real-time and safely identify inoculation procedures for responding to threats introduced to the protected domain via malware. The proactive security mechanism includes an Artificial Neural Network Interface (ANNI) configured to execute at least some features of the proactive security mechanism.