Toolkits for feature extraction and characterization of network data - Bandara, Vidarshana W. (Colorado State University. Libraries, 2015)
Zip file Data 1: GUI for Robust PCA recoverability experiments. The GUI provides the following functionalities: - Evaluate sufficient conditions for recovery over a selected range of ranks and sparsities, size, low-rank and sparse matrix types; - Recoverable region for a selected range fractional sparsities, size, low-rank and sparse matrix types; - Input - output mapping between fractional-ranks fractional-sparsities; - Recovery error of the low-rank component; - Recovery error of the sparse component.
Zip file Data 2: GUI for Robust Principal Component Analysis. The GUI performs Robust PCA on: - Synthesized low-rank and sparse matrix additions; - Data from external experiments.
Zip file Data 3: Random matrix generator. The software synthesizes random realizations of low-rank and sparse matrices of specified types. Synopsis: Input arguments: LS typ n frs reps fname LS: specify either low-rank or sparse. Use L or l to indicate low-rank matrices, and S or s to indicate sparse matrices. typ: indicates the type of the matrices. Types available for low-rank are: 1. First order Gaussian; 2. Second order Gaussian; 3. Wishart; 4. First order Vandermonde; 5. Second order Vandermonde; Type available for sparse matrices are: 1. Fixed; 2. Uniform; 3. Gaussian; n: size of the matrices. frs: fractional rank or fractional sparsity of the matrix. reps: number of realizations of the specified matrix. fname: base file name of the matrices. reps many files will be produced each containing a comma-delimited realization of the specified matrix. E.g.: If fname is testfile and reps is 3, then three output files with names testfile.1, testfile.2, and testfile.3 will be produced. Examples DOS: genRandMats-1.0.exe L 1 20 0.1 3 testfile Linux: ./run_genRandMatix_v01a.sh $MCR_DIRECTORY L 1 20 0.1 3 testfile.
Zip file Data 4: MATLAB® Toolkit for network traffic anomaly analysis. The toolkit performs: - De-trending and thresholding for anomaly detection; - Graph wavelet based summarizing and anomaly tracing; - Distribution fitting to spatial and temporal parameters; - Simulator/Emulator to regenerate statistically similar anomalies The toolkit is developed for Internet2 dataset, but customizable for other datasets.
Keywords: Internet traffic anomalies; Robust PCA; random matrices; anomaly modelling