Skip to content

iaikkrypto/lineartrails

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

9 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

lineartrails

Tool to automatically search for linear characteristics. There exists a research paper about this tool called "Heuristic Tool for Linear Cryptanalysis with Applications to CAESAR Candidates", which is presented at AsiaCrypt 2015, an online version is available here. If you use this tool in your work, it would be nice to cite the research paper.

Authors

Build

lineartools requires tiny2xml as submodule, so do after git clone:

git submodule init && git submodule update

To build:

make

Usage

The folder ./examples contains example search configuration for Ascon, ICEPOLE, Keyak, Minalpher and Proest. To start a search simply call for instance:

./lin -I 10 -S 2 -i examples/ascon_3_rounds_typeI.xml
  • -I determines how often status information of the search is displayed. -I -1 deactivates it.
  • -S determines how often the current and probably partial determined linear characteristic is put out. -S -1 deactivates it.
  • -i specifies the used xml based search file.

The output of the search are linear characteristics, where Round 0 tags the linear mask of the input of the first round, Round 1 the output of the first round, which is also the input for the second round and so on. Half Rounds, e.g. 1.5 represent intermediate result within one round. For instance if one round consists of a substitution layer followed by a permutation layer, a half round marks the output of the substitution layer and the input of the permutation layer.

A search file looks like follows:

<config>
<parameters>
  <permutation value="ascon"/>
  <rounds value="3"/>
</parameters>
<char value="
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????

????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????

????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????

????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????

????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
???????????????????????????????????????????????????????????????1

????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????

????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
????????????????????????????????????????????????????????????????
"/>
<search credits = "10000" print_active = "0">
  <phase>
    <setting push_stack = "0.1" alternative_sbox_guesses = "10" sbox_weight_probability = "3"  sbox_weight_hamming = "1">
      <guess sbox_layer="0" active_weight="0" inactive_weight="100"/>
      <guess sbox_layer="1" active_weight="0" inactive_weight="500"/>
      <guess sbox_layer="2" active_weight="0" inactive_weight="500"/>
    </setting>
    <setting push_stack = "0.5" alternative_sbox_guesses = "5">
      <guess sbox_layer="0" active_weight="1" inactive_weight="0"/>
      <guess sbox_layer="1" active_weight="2" inactive_weight="0"/>
      <guess sbox_layer="2" active_weight="3" inactive_weight="0"/>
    </setting>
  </phase>
</search>
</config>

In this file, the field char value contains the starting point of the search, where all intermediate masks are considered. Incomplete starting points are padded with '?'.

The field credits determines, how often a contradiction during the search is backtracked until the current search is re-started. To provide a clear view, the search only prints better characteristics than already found. With print_active it is determined whether this "best" metric targets active S-boxes or the bias.

Settings define which S-boxes are guessed and are treated subsequently. So if there is no guessable S-box in a current setting, the next one is taken. push_stack defines the probability that the current characteristic is pushed to the stack for a possible later backtracking. alternative_sbox_guesses defines how many other contradicting assignments of linear masks than the best one defined according to sbox_weight_probability and sbox_weight_hamming have to be taken into account until the characteristic is treaded as impossible. sbox_weight_probability and sbox_weight_hamming are used to rate masks for S-boxes. A high value for sbox_weight_probability prefers masks that have a high bias, whereas a high value sbox_weight_hamming prefers masks that have a low hamming weight.

active_weight determines the probability that an active S-box will be guessed (higher values mean higher chance). inactive_weight does the same for inactive S-boxes.

The code snippets describing the behavior of the linear and S-box layer of the implemented ciphers are taken from their reference implementations, which are available at http://bench.cr.yp.to/ebash.html.

The tool is tested under

  • Xubuntu 14.04 (64 bit) using gcc version 4.8.2
  • Xubuntu 14.10 (64 bit) using gcc version 4.9.1
  • Xubuntu 14.10 (64 bit) using clang (3.8 trunk, Revision 254516)
  • Ubuntu 15.04 (64 bit) using gcc version 4.9.2
  • OSX Yosemite using Apple LLVM version 6.0

About

Tool to automatically search for linear characteristics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors 4

  •  
  •  
  •  
  •