Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

Abstract: A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Abstract: A method for making monodispersed magnetic nanoparticles of nickel zinc ferrite (NZFO) at room temperature by mixing together two micelle solutions. The first micelle solution comprises zinc, nickel, and iron metal salts; a surfactant; and a hydrocarbon. The second micelle solution comprises and aqueous hydroxide, a surfactant, and a hydrocarbon. After mixing the two micelle solutions, the ferrite precipitates.

Abstract: A method for making monodispersed magnetic nanoparticles of nickel zinc ferrite (NZFO) at room temperature by mixing together two micelle solutions. The first micelle solution comprises zinc, nickel, and iron metal salts; a surfactant; and a hydrocarbon. The second micelle solution comprises an aqueous hydroxide, a surfactant, and a hydrocarbon. After mixing the two micelle solutions, the ferrite precipitates.

Abstract: A method for forming monodispersed magnetic nanoparticles of manganese zinc ferrite is provided which includes reversed micelle synthesis. The method includes preparing a micelle solution of zinc, manganese and iron salts, a surfactant and a hydrocarbon and mixing a second micelle solution of ammonium hydroxide, a surfactant and a hydrocarbon with the first solution to precipitate a ferrite precursor precipitate. The ferrite precursor precipitate is recovered, washed and annealed to produce nanoparticles of manganese zinc ferrite having a spinel crystal structure. Advantageously, the resulting nanoparticles of manganese zinc ferrite have a length no greater than 50 nm in any of the three spatial dimensions and the particle size distribution has a standard deviation of greater than 12% of the mean value.