Analyze files, not malware

Let’s dive right in. In the last post, I mentioned the value of reviewing the import address table (IAT) when performing static file analysis. Take a look at some IAT excerpts from three files:

Screen Shot 2016-03-03 at 9.19.44 PM

Screen Shot 2016-03-03 at 9.20.20 PM

Several functions may pique your interest, including:

  • IsDebuggerPresent and GetTickCount: functions that may be used to detect debugging activity.
  • RegCreateKeyW, RegSetValueExW: functions used to manipulate the registry, perhaps to configure persistence.
  • LoadLibraryW and GetProcAddress: functions used to call other functions at runtime, a strategy that hinders static file analysis.
  • FindResourceW and LoadResource: functions used to access embedded resources, where additional code may reside.

Let’s look behind the curtain:

  • File A = notepad.exe
  • File B = searchindexer.exe
  • File C = spoolsv.exe

These are all legitimate files found on a clean Windows 7 64-bit system.

This is not meant to be a trick, but instead a reminder. The rush of successfully identifying malware is one we all yearn for, but that glorious destination must be earned through careful analysis. This might be as simple as matching a suspect file’s hash with a known bad file hash, or it might require more robust static, behavioral and code analysis. All observations are not created equal, so we must weigh the severity of each one (i.e., how definitively it indicates malicious behavior) and consider their cumulative value when deciding if a file is malware. Files are innocent until proven guilty, and I challenge you to demonstrate, beyond a reasonable doubt, that a particular file is bad.

So how can you sharpen your ability to spot unusual characteristics that may indicate nefarious activity? As in all areas of incident identification and response, we need to understand the normal to discover the anomalous. Pick your favorite legitimate Windows programs and apply your file analysis process. You will likely identify characteristics that might otherwise seem alerting and, with practice, this will increase your tolerance for suspicious characteristics.

Inspecting known good files can also help validate (or invalidate) indicators of potential compromise. Think you’ve discovered a group of API calls, a set of strings, or a particular PE file characteristic that only exists in malware? Search across a large sample of legitimate files to test your theory.

There is nothing wrong with identifying indications of evil during the file analysis process, and that’s arguably the point of initiating an investigation. However, it is critical to view your suspicions about a file as hypotheses that you prove or disprove based on empirical evidence. Otherwise, you might miscategorize a legitimate file as malware, and that not only reflects poorly on you if someone checks your work – but it makes the file sad too.

If you would like to learn more about malware analysis strategies, join me at an upcoming SANS FOR610 course.

-Anuj Soni

About the Author:
Anuj Soni is a Senior Threat Researcher at Cylance, where he performs malware research and reverse engineering. He is also a SANS Certified Instructor and co-author of the course FOR610:Reverse-Engineering Malware. If you would like to learn more about malware analysis strategies, join him at an upcoming SANS FOR610 course.


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