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 MALHEUR - Automatic Analysis of Malware Behavior
 Copyright (c) 2009-2012 Konrad Rieck (konrad@mlsec.org)
 University of Goettingen, Berlin Institute of Technology

 Introduction
 --

 Malheur is a tool for the automatic analysis of malware behavior (program
 behavior recorded from malicious software in a sandbox environment). It
 has been designed to support the regular analysis of malicious software and
 the development of detection and defense measures. Malheur allows for
 identifying novel classes of malware with similar behavior and assigning
 unknown malware to discovered classes. It supports four basic actions for
 analysis which can be applied to reports of recorded behavior:

 1. Extraction of prototypes
     From a given set of reports, malheur identifies a subset of
     prototypes representative for the full data set. The prototypes
     provide a quick overview of recorded behavior and can be used to
     guide manual inspection.

 2. Clustering of behavior
     Malheur automatically identifies groups (clusters) of reports
     containing similar behavior. Clustering allows for discovering novel
     classes of malware and provides the basis for crafting specific
     detection and defense mechanisms, such as anti-virus signatures.

 3. Classification of behavior
     Based on a set of previously clustered reports, malheur is able to
     assign unknown behavior to known groups of malware. Classification
     enables identifying novel and unknown variants of malware and can be
     used to filter program behavior prior to manual inspection.

 4. Incremental analysis
     Malheur can be applied incrementally for analysis of large data
     sets. By processing reports in chunks, the run-time as well as
     memory requirements can be significantly reduced. This renders
     long-term application of malheur feasible, for example for daily
     analysis of incoming malware programs.

 A detailed description of these techniques as well as technical
 background on analysis of malicious software is provided in the
 following articles:

     "Automatic Analysis of Malware Behavior using Machine Learning"
     Konrad Rieck, Philipp Trinius, Carsten Willems, and Thorsten Holz
     Journal of Computer Security (JCS), 19 (4) 639-668, 2011.

     "A Malware Instruction Set for Behavior-Based Analysis"
     Philipp Trinius, Carsten Willems, Thorsten Holz, and Konrad Rieck
     Technical report TR-2009-07, University of Mannheim, 2009

 Dependencies
 --
      >= libconfig-1.4
      >= libarchive-2.70

 Compilation & Installation
 --
 
 From GIT repository first run

      $ ./bootstrap

 From tarball run

      $ ./configure [options]
      $ make
      $ make check
      $ make install

 Options for configure

      --prefix=PATH Set directory prefix for installation

      By default Malheur is installed into /usr/local. If you prefer
      a different location, use this option to select an installation
      directory.

      --enable-openmp Enable support for OpenMP (experimental)
    
      This option enables support for OpenMP in Malheur. Several
      functions of the malware analysis have been enhanced using
      OpenMP directives, such that they execute in parallel and
      benefit from multi-core architectures.

      --enable-matlab Enable optional Matlab tools
      --with-matlab-dir=PATH Set directory prefix of matlab installation

      Some functions of Malheur are also available in form of Matlab
      .mex files which allows for using implemented analysis
      methods directly from within a Matlab environment.

 License
 --

 This program is free software; you can redistribute it and/or modify
 it under the terms of the GNU General Public License as published by
 the Free Software Foundation; either version 3 of the License, or
 (at your option) any later version. This program is distributed
 without any warranty. See the GNU General Public License for more
 details.
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