Fnord is a pattern extractor for obfuscated code

Fnord has two main functions:

1. Extract byte sequences and create some statistics
2. Use these statistics, combine length, number of occurrences, similarity and keywords to create a YARA rule

Fnord processes the file with a sliding window of varying size to extract all sequences of with a minimum length -m X (default: 4) up to a maximum length -x X (default: 40). For each length, Fnord will present the most frequently occurring sequences -t X (default: 3) in a table.

Each line in the table contains:

• Length
• Number of occurrences
• Sequence (string)
• Formatted (ascii/wide/hex)
• Hex encoded form
• Entropy

By using the --yara flag, Fnord generates an experimental YARA rule. During YARA rule creation it will calculate a score based in the length of the sequence and the number of occurrences (length * occurrences). It will then process each sequences by removing all non-letter characters and comparing them with a list of keywords (case-insensitive) to detect sequences that are more interesting than others. Before writing each string to the rule Fnord calculates a Levenshtein distance and skips sequences that are too similar to sequences that have already been integrated in the rule.

[Experimental] Fnord was created a few days ago and I have tested it with a handful of samples. My guess is that I'll adjust the defaults in the coming weeks and add some more keywords, filters, scoring options.

If you've found obfuscated code in a sample, use a hex editor to extract the obfuscated section of the sample and save to a new file. Use that new file for the analysis.

Play with the flags -m, --yara-strings and -e.

Please send me samples that produce weak YARA rules that could be better.

        ____                 __
       / __/__  ___  _______/ /
      / _// _ \/ _ \/ __/ _  /
     /_/ /_//_/\___/_/  \_,_/ Pattern Extractor
     v0.3, Florian Roth
    usage: fnord.py [-h] [-f file] [-m min] [-x max] [-t top] [-n min-occ]
                    [-e min-entropy] [--strings] [--yara] [--yara-strings max]
                    [--show-score] [--show-count] [--include-padding] [--debug]

    Grimoire

    optional arguments:
      -h, --help          show this help message and exit
      -f file             File to process
      -m min              Minimum sequence length
      -x max              Maximum sequence length
      -t top              Number of items in the Top x list
      -n min-occ          Minimum number of occurrences to show
      -e min-entropy      Minimum entropy
      --strings           Show strings only
      --yara              Generate an experimental YARA rule
      --yara-strings max  Maximum sequence length
      --show-score        Show score in comments of YARA rules
      --show-count        Show count in sample in comments of YARA rules
      --include-padding   Include 0x00 and 0x20 in the extracted strings
      --debug             Debug output

1. git clone https://github.com/Neo23x0/Fnord.git and cd Fnord
2. pip3 install -r ./requirements.txt
3. python3 ./fnord.py --help

python3 fnord.py -f ./test/wraeop.sct --yara --yara-strings 10

python3 fnord.py -f ./test/inv-obf.txt --yara --show-score --show-count -t 1

python3 fnord.py -f ./test/bash-obfusc.txt --yara --show-score --show-count --yara-strings 10 -t 2

python3 fnord.py -f ./test/launch-varplus.txt --yara --show-score --show-count --yara-strings 10 -t 2

yarGen uses a white-listing approach to filter the strings that are best for the creation of a YARA rule. yarGen applies some regular expressions to adjust scores of strings before creating the YARA rules. But its approach is very different to the method used by Fnord, which calculates the score of the byte sequences based on statistics.

While yarGen is best used for un-obfuscated code. Fnord is for obfuscated code only and should produce much better results than yarGen.

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