Python-ELMO

Python-ELMO is a Python library which proposes an encapsulation of the project ELMO, a statistical leakage simulator for the ARM M0 family.

[MOW17] Towards Practical Tools for Side Channel Aware Software Engineering : ’Grey Box’ Modelling for Instruction Leakages by David McCann, Elisabeth Oswald et Carolyn Whitnall. https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/mccann

ELMO GitHub: https://github.com/sca-research/ELMO

Requirements

To use Python-ELMO, you need at least Python3.5 and numpy.

The library will install and compile ELMO. So, you need the GCC compiler collection and the command/utility make (for more details, see the documentation of ELMO). On Ubuntu/Debian,

sudo apt install build-essential

To use ELMO on a leaking binary program, you need to compile the C implementations to binary programs (a “.bin” file). “ELMO is not linked to any ARM specific tools, so users should be fine to utilise whatever they want for this purpose. A minimal working platform for compiling your code into an ARM Thumb binary would be to use the GNU ARM Embedded Toolchain (tested version: arm-none-eabi-gcc version 7.3.1 20180622, it can be downloaded from https://developer.arm.com/open-source/gnu-toolchain/gnu-rm).”, see the documentation of ELMO for more details.

Installation

First, download Python-ELMO.

git clone https://github.com/ThFeneuil/python-elmo

And then, install ELMO thanks to the installation script. It will use Internet to download the ELMO project.

python setup.py install

Usage

Create a new simulation project

What is a simulation project ? It is a project to simulate the power traces of one binary program. It includes

• A Python class to manage the project;
• The C program which will be compile to have the binary program for the analysis;
• A linker script where the configuration of the simulated device are defined.

To start a new project, you can use the following function.

from elmo import create_simulation
create_simulation(
   'dilithium', # The (relative) path of the project
   'DilithiumSimulation' # The classname of the simulation
)

This function will create a repository dilithium with all the complete squeleton of the project. In this repository, you can find:

• The file project.c where you must put the leaking code;
• The file projectclass.py where there is the class of the simulation which will enable you to generate traces of the project in Python scripts;
• A Makefile ready to be used with a compiler arm-none-eabi-gcc.

Usually a leaking code runs challenges, one challenge giving a power trace. A challenge is the execution of a code with a specific set of data. This set of data is given in the input of the leakage simulation. For example, one can imagine that the leaking code is a symmetric encryption and one wants to study its power leakage according to the message and the secret key. Then, a challenge is the simulation of the leakage for a specific message and for a specific secret key.

So, the classical form of project.c is the following one:

• It gets a number of challenges with readbyte.
• Then, it loops for each challenge.
  • For the challenge, load the specific set of data with readbyte.
  • Start the record of the power leakage (start a power trace) with starttrigger.
  • Realise the leaking operations with the loaded set of data.
  • Stop the record of the power leakage (end a power trace) with endtrigger.
  • Eventually output some data with printbyte.
• Indicate to ELMO tool that the simulation is finished with endprogram.

The file projectclass.py contains a subclass of SimulationProject. It is the description of the project.c file for the ELMO tool, in order to correctly realise the simulation. It also provides methods to manage the simulation (see following sections).

• The classmethod get_binary_path(cl) must return the relative path of the leakage binary (project.c correctly compiled).
• The method set_input_for_each_challenge(self, input, challenge) must write a challenge in input using the function write.

Many methods of SimulationProject can be rewritten in the subclass if necessary. For example, in the case where your project.c doesn’t run challenges, you can rewrite the method set_input(self, input).

Important! Don’t forget that ELMO (and so Python-ELMO) needs a compiled version of project.c (see the “Requirements” section for more details). The provided Makefile is here to help you to compile.

List all the available simulation

from elmo import search_simulations
search_simulations('.')

{'DilithiumSimulation': <class 'DilithiumSimulation'>,
 'KyberNTTSimulation': <class 'KyberNTTSimulation'>}

Python-ELMO offers a example project to you in the repository projects/Examples of the module. This example is a project to generate traces of the execution of the NTT implemented in the cryptosystem Kyber.

Use a simulation project

Warning! Before using it, you have to compile your project thanks to the provided Makefile.

from elmo import get_simulation
KyberNTTSimulation = get_simulation('KyberNTTSimulation')

simulation = KyberNTTSimulation()
challenges = simulation.get_random_challenges(10)
simulation.set_challenges(challenges)

simulation.run() # Launch the simulation
traces = simulation.get_traces()
# And now, I can draw and analyse the traces

Use a simulation project thanks to a server

Sometimes, it is impossible to run the simulation thanks the simple method run of the project class. Indeed, sometimes the Python script is executed in the environment where Python-ELMO cannot launch the ELMO tool. For example, it is the case where Python-ELMO is used in SageMath on Windows. On Windows, SageMath installation relies on the Cygwin POSIX emulation system and it can be a problem.

To offer a solution, Python-ELMO can be used thanks to a client-server link. The idea is you must launch the following script which will listen (by default) at port 5000 in localhost.

python -m elmo run-server

And after, you can manipulate the projects as described in the previous section by replacing run to run_online.

from elmo.manage import get_simulation
KyberNTTSimulation = get_simulation('KyberNTTSimulation')

simulation = KyberNTTSimulation()
challenges = simulation.get_random_challenges(10)
simulation.set_challenges(challenges)

simulation.run_online() # Launch the simulation THANKS TO A SERVER
traces = simulation.get_traces()
# And now, I can draw and analyse the traces

Warning! Using the run_online method doesn’t exempt you from compiling the project with the provided Makefile.

Use the ELMO Engine

The engine exploits the model of ELMO to directly give the power consumption of an assembler instruction. In the model, to have the power consumption of an assembler instruction, it needs

• the type and the operands of the previous assembler instruction
• the type and the operands of the current assembler instruction
• the type of the next assembler instruction

The type of the instructions are:

• EOR for ADD(1-4), AND, CMP, CPY, EOR, MOV, ORR, ROR, SUB;
• LSL for LSL(2), LSR(2);
• STR for STR, STRB, STRH;
• LDR for LDR, LDRB, LDRH;
• MUL for MUL;
• OTHER for the other instructions.

from elmo.engine import ELMOEngine, Instr
engine = ELMOEngine()
for i in range(0, 256):
    engine.add_point(
        (Instr.LDR, Instr.MUL, Instr.OTHER), # Types of the previous, current and next instructions
        (0x0000, i), # Operands of the previous instructions
        (0x2BAC, i)  # Operands of the current instructions
    )
engine.run() # Compute the power consumption of all these points
power = engine.power # Numpy 1D array with an entry for each previous point
engine.reset_points() # Reset the engine to study other points

Limitations

Since the ELMO project takes its inputs and outputs from files, Python-ELMO can not manage simultaneous runs.

Licences

MIT