Abstract: A system and associated methods for the detection of anomalous behavior in a system. In some embodiments, time-series data that is obtained from the system (such as log data) may be used as an input to a process that converts the data into greyscale values. The greyscale values are used to construct an “image” of the system operation that is used as an input to a convolutional neural network (CNN). The image is used to train the neural network so that the neural network is able to recognize when other input “images” constructed from time-series data are anomalous or otherwise indicative of a difference between the prior (and presumed normal or acceptable) and the current operation of the system.

Abstract: Elements and processes used to enable the generation and interaction with complex networks, simulations, models, or environments. In some embodiments, this is accomplished by use of a client-server architecture that implements processes to improve data transport efficiency (and hence network usage), reduce latency experienced by users, and optimize the performance of the network, simulation, model or environment with respect to multiple parameters.

Abstract: Systems and methods for creating entities that operate within a virtual environment, where in some embodiments the entities are substantially autonomous in the sense that they are capable of communications and interactions with the environment and other entities. In some embodiments, the entities may be capable of interacting with an environment other than the one in which they were created and originally configured. In some embodiments, the entities may engage in interactions with other entities that operate to enable changes in behavior of one or both of the entities.