Abstract: Systems and methods for tracking malware operator behavior patterns in a network environment simulated for an extended period of time include a processor that causes the system to receive organizational data that describes a virtual organization, obtain additional data related to the organizational data, and provide a simulated computer network of the virtual organization based on the organizational data. The process can further cause the system to install at least one malware on the simulated computer network, monitor one or more interactions between the simulated computer network and an operator of the malware, and build a malware operator profile that characterizes the operator of the malware based on the one or more interactions, with which the operator of the malware can be identified in subsequent interactions.

Abstract: Systems and methods for tracking malware operator behavior patterns in a network environment simulated for an extended period of time include a processor that causes the system to receive organizational data that describes a virtual organization, obtain additional data related to the organizational data, and provide a simulated computer network of the virtual organization based on the organizational data. The process can further cause the system to install at least one malware on the simulated computer network, monitor one or more interactions between the simulated computer network and an operator of the malware, and build a malware operator profile that characterizes the operator of the malware based on the one or more interactions, with which the operator of the malware can be identified in subsequent interactions.

Abstract: Systems and methods for tracking malware operator behavior patterns in a network environment simulated for an extended period of time include a processor that causes the system to receive organizational data that describes a virtual organization, obtain additional data related to the organizational data, and provide a simulated computer network of the virtual organization based on the organizational data. The process can further cause the system to install at least one malware on the simulated computer network, monitor one or more interactions between the simulated computer network and an operator of the malware, and build a malware operator profile that characterizes the operator of the malware based on the one or more interactions, with which the operator of the malware can be identified in subsequent interactions.

Abstract: Systems and methods for tracking malware operator behavior patterns in a network environment simulated for an extended period of time include a processor that causes the system to receive organizational data that describes a virtual organization, obtain additional data related to the organizational data, and provide a simulated computer network of the virtual organization based on the organizational data. The process can further cause the system to install at least one malware on the simulated computer network, monitor one or more interactions between the simulated computer network and an operator of the malware, and build a malware operator profile that characterizes the operator of the malware based on the one or more interactions, with which the operator of the malware can be identified in subsequent interactions.

Abstract: A base station signal processing mechanism for a time division multiple access (TDMA) cellular communication system adaptively controls weighting coefficients of the base station's phased array antenna in a manner that forms a directivity pattern whose gain and/or nulls maximize the signal to noise ratio in the presence of co-channel users whose communication time slots overlap a communication time slot of a desired user. The signal processing mechanism performs correlation processing of synchronization patterns contained in signals transmitted by co-channel users to identify times of transitions between successive co-channel users' communication time slots relative to a time of transition of the desired user's communication time slot, and deriving weighting coefficients in accordance with the times of transitions.

Abstract: Human speech is detected in an audio signal by first providing a single autocorrelated signal indicative of the audio signal multiplied by a time-delayed portion of the audio signal, the delay being an amount of time indicative of a period corresponding to a first formant frequency. Portions of the autocorrelated signal are compared with a scaled noise value. Human speech is detected by examining whether a plurality of portions of the autocorrelated signal exceed the scaled noise value.