A Simulator for the semantic intersection attack on the DNS Range Query algorithm.
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README.md

README.md

DRQPatternAttack.py

This is a simulator for the (semantic) intersection attack on the DNS Range Query algorithm, as proposed by Zhao et al.

It requires a dataset generated by DNSPatternFinder.py.

More details on the program will be added closer to public release.

Usage

usage: DRQPatternAttack.py [-h] [-m {1,2,3,4,5,6}] [-s NUM] [-c CNT]
                           [-p PARTITION] [-t THREADS] [--target url | --all]
                           [--stat] [-v | -q] [--version]
                           file

positional arguments:
  file                  select pattern file.

optional arguments:
  -h, --help            show this help message and exit
  -m {1,2,3,4,5,6}, --mode {1,2,3,4,5,6}
                        Enable a specific mode of operation. See below for
                        possible options. [default 1]
  -s NUM, --size NUM    Size of the range query [default 50]
  -c CNT, --count CNT   Number of random targets to be tried [default 50]
  -p PARTITION, --partition PARTITION
                        Number of Queries the Client should be allowed to use
                        [default -1 for all queries]
  -t THREADS, --threads THREADS
                        Number of Threads used for processing [default 1]
  --target url          Attack this domain
  --all                 Attack all possible targets (may take a long time).
                        Implies -q, --stat
  --stat                Show statistics about the accuracy of the algorithm
  -v, --verbose         enable verbose output (show more information).
  -q, --quiet           enable quiet mode.
  --version             show program's version number and exit

Modes of Operation:
  1) No distinguishable Blocks 		- Random Generation
  2) Distinguishable first Block 	- Random Generation
  3) Fully distinguishable Blocks 	- Random Generation
  4) No distinguishable Blocks 		- Pattern-based generation
  5) Distinguishable first Block 	- Pattern-based generation
  6) Fully distinguishable Blocks 	- Pattern-based generation

  Please consult the paper to find more information about the modes