ToolWar | Information Security (InfoSec) Tools: Adobe

<div class="separator" style="clear: both; text-align: center;">
<img alt="adobe malware classifier " border="0" height="266" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgJFSWBvBrB6iEW5mUe0RLgLd4aTklWqEcrJku4K5HoKfHloY6DDXgTMVL4g8funQXw0_Yaxw2LuVUfUe3WqD_yvQoVBSUtp-dpw3XWg7kkmWbJlum9If5NsE1Kifvf0LFPi1Gryz7sq1ot/s400/adobe+malware+classifier.png" title="adobe malware classifier " width="400" /></div>
<div style="text-align: justify;">
<b>Adobe Malware Classifier</b> is a command-line tool that lets antivirus 
analysts, IT administrators, and security researchers quickly and easily
 determine if a <b><i>binary file contains malware</i></b>, so they can develop <b>
<a href="http://www.toolwar.com/search/label/Malware" target="_blank">malware</a> detection</b> signatures faster, reducing the time in which users' 
systems are vulnerable.
Malware Classifier uses machine learning algorithms to classify Win32 
binaries – EXEs and DLLs – into three classes: 0 for “clean,” 1 for 
“malicious,” or “UNKNOWN.”<br /><br />
The tool was developed using models resultant from running the J48, J48 
Graft, PART, and Ridor machine-learning algorithms on a dataset of 
approximately 100,000 malicious programs and 16,000 clean programs. <br />
The tool extracts seven key features from an unknown binary, feeds them 
to one of the four classifiers or all of them, and presents its 
classification of the unknown binary.</div>
<div style="text-align: justify;">
<h3>
Download :: </h3>
<b>Python :: </b><a href="http://sourceforge.net/projects/malclassifier.adobe/files/AdobeMalwareClassifier.py/download" target="_blank">AdobeMalwareClassifier.py</a><br />
<b>Source :: </b><a href="http://sourceforge.net/projects/malclassifier.adobe/?source=navbar">http://sourceforge.net/projects/malclassifier.adobe/?source=navbar</a></div>

Malware Classifier (Malware Analysis) :: Tools

Adobe Malware Classifier is a command-line tool that lets antivirus analysts, IT administrators, and security researchers quickly and e...