Search Results for "malware-samples"

Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent...

Display people's or device transit routes based on latitude and longitude graphically

Abdal Header Analyzer is a software for analyzing site and web server headers. As you know, headers contain information that security experts and hackers use to analyze targets.

DenseBlocks is an unsupervised classification method for n-dimensional numerical data. It creates a multi-dimensional density representation of given training samples, defines clusters accordingly and returns this representation which can further be used to classify related data.

...Kim S et al, "Virmid: accurate detection of somatic mutations with sample impurity inference", Genome Biology 2013, 14:R90 http://genomebiology.com/2013/14/8/R90/abstract Virmid (Virtual Microdissection for SNP calling) is a Java based variant caller designed for disease-control matched samples. Virmid is also specialized for identifying potential within individual contamination where the disease sample cannot be purified enough. While the SNP calling rate is severely compromised with this heterogeneity, Virmid can uncover SNPs with low allele frequency by considering the level of contamination (alpha). The important features of Virmid are: •Estimation of accurate proporation of control sample in a (mixed) disease sample •Improved SNP and somatic mutation calling with regard to the estimated proportion

Malware Analysis Network in Taiwan <Man in Taiwan, MiT> Welcome to contact us (TonTon@TWMAN.ORG) if you are interested in collaborating with us. This project is open source and distributed under the GNU General Public License version 3. Please feel free to add to or modify this source and propose changes or new converters. Developer & Copyrighted by : TonTon Hsien-De Huang Prompter: Jazz Yao-Tsung Wang, Figaro Chen-Ho Yang | Logo Desinger:Temaki Guo Community on Google+：http://X.TWMAN.ORG/Community/ SourceForge: https://sourceforge.net/projects/twmanplus/files/ FaceBook: https://www.facebook.com/TWMAN.PLUS

...For comparison purposes, non-SSMD-based methods such as percent activity, z-score, and t-test are also provided as scoring options. Version 2.1 released: several bugs fixed. Version 3.0 released for Windows and Mac: experiment-wise analysis option, map to all samples option (no hit cut-off needed), percent activity for replicates option and .ai output format are added

Provide a reference implementation of Moving Taylor Bayesian Regression, a method for nonparametric multi-dimensional function estimation with correlated errors from finite samples, as a Python package based on SciPy

...The OSAF-Toolkit was developed, as a senior design project, by a group of IT students from the University of Cincinnati, wanting to pioneer and pave the way for standardization of Android malware analysis. The OSAF-Toolkit is built from Ubuntu 11.10 and pre-compiled with all of the tools needed to rip apart applications for code review and malware analysis. Our primary goal with the toolkit is to be able to make application analysis as easy as possible. We also wanted to create a community where security professionals, analysts, developers and newcommers can learn, discuss and share methodologies with one another. ...

Application for viewing and especially editing HRM-files generated by Polar Heart Rate Monitors (e.g. S720i, S725X, S625X). It provides a GUI for finding errors and manually correcting samples.

Example-based Modeling (EMO) is an tool to create data models, with examples, using a web interface. You interactively create a web-accessible database of models and samples for those models. A white paper describes the underlying assumptions.

Bayesian Surprise Matlab toolkit is a basic toolkit for computing Bayesian surprise values given a large set of input samples. It is also useful as way of exploring surprise theory. For more information see also: http://ilab.usc.edu/