Abstract: Cells of a network can be clustered based on signaling behavior, and abnormal signaling conditions against cells can be detected and mitigated. A security management component (SMC) can determine a neural network (NN) of NNs that can be representative of the cell network based on analysis of first signal measurement data associated with the cells. The NN can cluster respective cells into respective clusters based on analysis of second signal measurement data associated with the cells. The NN can determine whether an abnormal signaling condition associated with a cell is occurring based on analysis of third signal measurement data associated with the cells, information relating to the cluster to which the cell is assigned, and a defined network security criterion. SMC can perform feature reforming on the first, second, and/or third signal measurement data to reduce dimensionality of such data to facilitate processing by the NN.

Abstract: Cells of a network can be clustered based on signaling behavior, and abnormal signaling conditions against cells can be detected and mitigated. A security management component (SMC) can determine a neural network (NN) of NNs that can be representative of the cell network based on analysis of first signal measurement data associated with the cells. The NN can cluster respective cells into respective clusters based on analysis of second signal measurement data associated with the cells. The NN can determine whether an abnormal signaling condition associated with a cell is occurring based on analysis of third signal measurement data associated with the cells, information relating to the cluster to which the cell is assigned, and a defined network security criterion. SMC can perform feature reforming on the first, second, and/or third signal measurement data to reduce dimensionality of such data to facilitate processing by the NN.

Abstract: Cells of a network can be clustered based on signaling behavior, and abnormal signaling conditions against cells can be detected and mitigated. A security management component (SMC) can determine a neural network (NN) of NNs that can be representative of the cell network based on analysis of first signal measurement data associated with the cells. The NN can cluster respective cells into respective clusters based on analysis of second signal measurement data associated with the cells. The NN can determine whether an abnormal signaling condition associated with a cell is occurring based on analysis of third signal measurement data associated with the cells, information relating to the cluster to which the cell is assigned, and a defined network security criterion. SMC can perform feature reforming on the first, second, and/or third signal measurement data to reduce dimensionality of such data to facilitate processing by the NN.

Abstract: Concepts and technologies disclosed herein are directed to internet traffic classification via time-frequency analysis. According to one aspect of the concepts and technologies disclosed herein, a security classification scheme can be implemented to identify potentially malicious activities from normal internet traffic. The security classification scheme can exploit the distinctive characteristics of different types of traffic in both frequency domain and time domain to identify four different cases. Due to the separation of different types of traffic, the security classification scheme can lower the false alarm rate and improve network security. The security classification scheme can utilize a recursive discrete Fourier transform (“DFT”) implementation to enhance computational efficiency.

Abstract: Concepts and technologies disclosed herein are directed to internet traffic classification via time-frequency analysis. According to one aspect of the concepts and technologies disclosed herein, a security classification scheme can be implemented to identify potentially malicious activities from normal internet traffic. The security classification scheme can exploit the distinctive characteristics of different types of traffic in both frequency domain and time domain to identify four different cases. Due to the separation of different types of traffic, the security classification scheme can lower the false alarm rate and improve network security. The security classification scheme can utilize a recursive discrete Fourier transform (“DFT”) implementation to enhance computational efficiency.

Abstract: The present invention pertains to pharmaceutical formulations containing propofol, methods for making such formulations, and methods for administering propofol at a pH above about 6.5, wherein the pain normally associated with propofol administration is reduced.

Abstract: The present invention pertains to pharmaceutical formulations containing propofol, methods for making such formulations, and methods for administering propofol at a pH above about 6.5, wherein the pain normally associated with propofol administration is reduced.