malheur - A Tool for Automatic Analysis of Malware Behavior

项目说明

Malheur - Automatic Analysis of Malware Behavior
==

This software belongs to the publication

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

Introduction
--

Malheur is a tool for the automatic analysis of malware behavior (program
behavior recorded from malicious software in a sandbox en). It
has been designed to support the regular analysis of malicious software and
the de of detection and defense measures. Malheur allows for
identifying no 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 o 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 disco 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 pre clustered reports, malheur is able to
assign unknown behavior to known groups of malware. Classification
enables identifying novel and unknown 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 Beha Analysis."
Philipp Trinius, Carsten Willems, Thorsten Holz, and Konrad Rieck
Technical report TR-2009-07, Uni of Mannheim, 2009

Dependencies
--

+ libconfig >= 1.4, <
+ libarchive >= 3.1.2, <

#### Debian & Ubuntu Linux

The following packages need to be installed for compiling Malheur on Debian
and Ubuntu Linux

gcc
libconfig9-dev
libarchi

For bootstrapping Malheur from the GIT repository or manipulating the
automake/autoconf configuration, the following additional packages are
necessary.

automake
autoconf
libtool

#### Mac OS X

For compiling Malheur on Mac OS X a working installation of Xcode is required
including gcc. Additionally, the following packages need to be installed

libconfig
libarchive (from homebrew-alt)

#### OpenBSD

For compiling Malheur on OpenBSD the following packages are required. Note
that you need to use gmake instead of make for building Malheur.

gmake
libconfig
libarchive

For bootstrapping Malheur from the GIT repository, the following packages
need be additionally installed

autoconf
automake
libtool

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.

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 3 of the License, or
(at your option) any later . This program is distributed
without any warranty. See the GNU General Public License for more
details.

Copyright
--

Copyright (c) 2009-2015 Konrad Rieck ()
Uni of Goettingen, Berlin Institute of Technology

项目信息

开发语言：C

Star 数：376

技术要点

该项目属于病毒分析方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕A Tool for Automatic Analysis of Malware Behavior展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。