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Side-channel toolkit in Julia
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README.md

What is it?

Jlsca is a toolbox in Julia to do the computational part (DPA) of a side channel attack. It supports:

  • Conditional averaging, for analog measurements
  • Conditional bitwise sample reduction, for whiteboxes
  • Threaded and tiled incremental correlation statistics
  • Parallelization of all the above (multi-core / multi-machine)
  • Correlation power analysis (CPA)
  • non-profiled Linear regression analysis (LRA)
  • Mutual Information Analysis (MIA)
  • AES128/192/256 enc/dec, backward/forward S-box attacks, backward/forward S-box round attacks
  • AES128 enc/dec chosen input MixColumn attack
  • Some whitebox models for AES (INV MUL / Klemsa)
  • DES/TDES1/TDES2/TDES3 enc/dec, backward/forward attack
  • SHA1 backward/forward attack (for HMAC)
  • Known key analysis + key rank evolution CSV output
  • Inspector trace set input and output
  • Split sample and data raw binary (Daredevil) input and output
  • Trace alignment using FFT convolution
  • Trace alignment using Dynamic Time Warping

Then, not really that related to this toolbox, but I've been playing with a Picoscope, and there is an example in examples/piposcope.jl that does (a quite fast, if I may say so myself) acquisition on Riscure's Pinata board using the scope's rapid block mode. Check the file header of piposcope.jl for more information.

Why would I want it?

Because it's free!

Who wrote it?

It's written for fun by me (Cees-Bart Breunesse) and Ilya Kizhvatov (whose pysca I shamelessly used as a baseline). Ruben Muijrers contributed the bit compression for whiteboxes used in conditional bitwise sample reduction. This toolbox is not a Riscure product, and Riscure does not support or maintain this code. If it crashes or you have feature requests, Github allows you to contact the authors, or you do a pull request ;-) It's the first code I ever wrote in Julia so if you have comments about how to make it faster or less of a hack (try the parallelization code), by all means contact me.

Installation

  1. Install Julia (1.0.2) is tested, everything prior is no longer compatible) and make sure the julia executable is in your path (notably for Windows users). Start he Julia REPL by executing julia.

  2. In the REPL type using Pkg; Pkg.add(PackageSpec(url="https://github.com/Riscure/Jlsca"))

You can now close the Julia prompt.

Running

There are several ways to interact with Jlsca. You can run scripts from command line, interactive through Julia's REPL or via Jupyter notebooks. The first two are described in the this README. The elegant and powerful Jupyter approach is described here, and includes a solution for Riscure's RHME2 SCA challenges from 2016.

Multi-threading versus multi-processing

Various components (conditional averaging, conditional sample reduction, incremental correlation) in Jlsca can be split and run in parallel in different worker processes. Only one component in Jlsca in currently threaded, and that is the incremental correlation statistics. On a single CPU with multiple cores, running incremental correlation with multiple threads in a single process will probably outperform splitting the work over multiple processes that use a single thread. The reason for this is that parallel processes on a single CPU will thrash the cache. The threaded implementation of incremental correlation is tiled and cache friendly. Usually, choosing #workers == #CPUs and #threads == #hardware cores in a single CPU is the preferred way, but of course your milage may vary. Julia by default uses a single worker, and a single thead (per worker). To change this run as julia -p 2 to give Julia 2 workers (== 3 processes, 1 master process and 2 workers, on the same machine), and JULIA_NUM_THREADS=2 julia to give the Julia process 2 threads. It's a bit unfortunate that this interface is not unified ..

I parallelized components in Jlsca because I wanted to run and split over multiple machines, but so far I haven't found the time to experiment with that. On my TODO list is to create a small cluster with 2+ nodes, each with a dual-Xeon CPU and a fast shared filesystem, since the input trace sets need to be accessible for each worker. A small, lean and simple implementation of a cluster would be Linux + NFS + SSH on each node. Julia can then be deployed to these nodes using the --machinefile option, as described here. This would be the easiest way to unleash Jlsca's parallel abilities.

Running from cmd line

Step 2 in the installation performed a git clone of Jlsca in your Julia's user directory. For me, it's in ~/.julia/v0.5/Jlsca. Jlsca is a library, but there are a few script files in Jlsca's examples directory. These scripts perform the various attacks and combination of settings supported by Jlsca. Some attack parameters, for example the "known key", are extracted from the file name, and some hard coded attack defaults are used (i.e. CPA, single bit0 or HW attack). You can change all this by editing the main files only: the library code under src need not be touched for that.

File main-noninc.jl

This file performs vanilla correlation statistics on an input trace set. Vanilla meaning that it will compute correlation with the entire sample and hypothesis matrices present in memory. This is a good starting point for playing and implementing new statistics or attacks. This main does not do parallelization, so it will complain when you specify multiple processes (-pX) on the julia command line. For example:

julia examples/main-noninc.jl destraces/tdes2_enc_9084b0087a1a1270587c146ccc60a252.trs

Or if you want to do a backwards attack do this (applies to all the main-xxx.jl files)

julia examples/main-noninc.jl destraces/tdes2_enc_9084b0087a1a1270587c146ccc60a252.trs BACKWARD

File main-condavg.jl

This file performs vanilla correlation statistics but will first run the conditional averager over the trace set input. This job can be parallelized to, for example, 2 processes by adding -p 2 to the command line.

julia examples/main-condavg.jl aestraces/aes128_sb_ciph_0fec9ca47fb2f2fd4df14dcb93aa4967.trs

File main-condred.jl

This file perform vanilla correlation statistics over conditionally sample reduced trace sets. Check the source of conditional-bitwisereduction.jl if you want to know more; this is only useful for whiteboxes, since it works on bit vector sample data, not on floating points. This process can be parallelized.

julia examples/main-condred.jl destraces/des_enc_1c764a2af6e0322e.trs

File main-condred-wbaes.jl

This file combines Klemsa's leakage models for tackling Dual AES with the conditional bit wise reduction. Can be parallelized. In main-condred-wbaes.jl the attack params are hardcoded for AES128 trace sets, so you'll need to edit the main file if you want pass it something else, like AES192 or 256. If you want to apply this on your own whitebox data make sure the params.dataOffset in main-condred-wbaes.jl point to the input (if params.direction == FORWARD) or output (if params.direction == BACKWARD). Can parallelized, for example:

julia examples/main-condred-wbaes.jl aestraces/aes128_sb_ciph_0fec9ca47fb2f2fd4df14dcb93aa4967.trs

File main-inccpa.jl

Last but not least, this file will perform a correlation attack using incremental correlation statistics. This is what Inspector also implements in its "first order" attack modules. You can parallelize or multi-thread this attack. For example:

JULIA_NUM_THREADS=2 julia examples/main-inccpa.jl aestraces/aes128_mc_invciph_da6339e783ee690017b8604aaeed3a6d.trs

Hacking stuff

By all means take a look at the main-xxx.jl files, pick one that matches what you want to do closest, copy and change accordingly. Below are some more details on how to set parameters, data and sample passes.

Attack parameters

There are several supported attacks: AesSboxAttack(), AesMCAttack(), DesSboxAttack(), Sha1InputAttack, and Sha1OutputAttack defined in attackaes-core.jl, attackdes-core.jl and attacksha-core.jl. For example, the following creates a forward AES128 attack attack on all 8 bits of the intermediate state with vanilla CPA:

# first define an attack
attack = AesSboxAttack()
attack.mode = CIPHER
attack.keyLength = KL128
attack.direction = FORWARD
# then an analysis
analysis = CPA()
analysis.leakages = [Bit(i) for i in 0:7]
# combine the two in a DpaAttack. The attack field is now also accessible
# through params.attack, same for params.analysis.
params = DpaAttack(attack,analysis)

What's put in analysis.leakages is a list of objects with a type that inherits from Leakage and implements a leak function. Look in sca-leakages.jl on how to see HW and Bit type leakges are implemented. If you'd like to attack with HW instead, write this:

params.analysis.leakages = [HW()]

If you want to attack the HD between S-box in and out, set this (only for the DES/AES attacks):

params.attack.xor = true

Since we're configuring a forward attack, we need to tell Jlsca what the offset of the input in the trace data is:

params.dataOffset = 1

This means that the input data is the start of the trace data: Julia offsets are 1-based!! If you'd rather run the attack backwards, and the output data is located after the input, you'd type this:

params.attack.direction = BACKWARD
params.dataOffset = 17

If we want to attack all key bytes, we write this:

params.targetOffsets = collect(1:16)

If we only want the first and last key bytes we write this:

params.targetOffsets = [1,16]

If we want LRA instead of CPA, we do this:

params.analysis = LRA()
params.analysis.basisModel = basisModelSingleBits

Big fat disclaimer: currently LRA only supports only a 9 bit model, even if you do a AES MC attack! I need to understand some more about this attack, since choosing a 33 bit basis model results in non-invertible matrices (and a crash). If we want MIA instead of LRA, we do this:

params.analysis = MIA()
params.analysis.sampleBuckets = 9
params.analysis.leakages = [HW()]

The sampleBuckets value is the number of buckets in which samples (observations) will be split, but only if the sample type is a real number. If your samples aren't floats, you should add a sample pass like this addSamplePass(trs, float). Like LRA, MIA is a bit hacky, and subject to change ;-)

Passing attack phase data

To attack inner rounds you need to use key material you recovered earlier. Jlsca does all this automagically by default. For example, AES192 consists of two separate attacks. If you only want to attack the first round of AES192 you can tell Jlsca by setting the attack parameter:

params.phases = [1]

which gives you the first round key, printed as a hex string labeled "next phase input" on the console. You cut and paste that information into the next phase like this, or if you ran phase 1 before this data will already be present in params.phaseInput:

params.phases = [PHASE2]
params.phaseInput = hex2bytes("00112233445566778899aabbccddeeff"))

Passes and processors

Attacks in Jlsca work on instances of the Traces type. There are two implementations of this type in Jlsca: InspectorTrace representing an Inspector trace set, and SplitBinary representing the completely flat and split data and samples used in, for example, Daredevil. These types are not simply providing access to the trace data in files, they come with addition functionality.

Suppose for example we open an Inspector trace set as follows. This reads the meta data corresponding to the trace set on disk, but does not read all the data in the file.

trs = InspectorTrace("bla.trs")

We can now read a the first trace (i.e. a tuple of the data and corresponding samples) like this.

(data, sample) = trs[1]

Suppose now that we would like to take the absolute of all samples, each time we read one trace. You can of course simply run abs(samples), but if you pass the trs instance into the Jlsca library that, for example, performs a DPA attack on that file, you'd have to modify this code to call abs(samples). Instead, you can add a "sample pass" to the trs object. A pass is simply a function, that will be run over the trace at the moment it's read.

# add function abs to the trs objet
addSamplePass(trs, x -> abs.(x))
# read a trace
(data, samples) = trs[1]
# samples now contain the absolute

You can add as many passes as you want, and they will be executed in the order you add them. For example:

# only get the first 2048 samples
addSamplePass(trs, x -> x[1:2047])
# then, compute the spectrum over that
addSamplePass(trs, real(fft(x)))

A similar mechanism exists for the data in a trace set by means of the function addDataPass. Internally in Jlsca this function is used to add cipher round functions during an attack. From a users perspective you only really need a data pass for filtering traces. For example, consider a trace set with TVLA traces where the first byte of the data field is set to 0x01 for a random input, and 0x00 for a semi-constant input. Then, if we want to attack using on the random traces we'd write:

# return only the traces with the first data byte set to 0x1
addDataPass(trs, x -> x[1] == 0x1 ? x : Vector{UInt8}(undef,0))

Once you push a sample or data pass on the stack of passes, you can pop the last one you added by calling popSamplePass() or popDataPass().

Conditional averaging and potential other post processors

Conditional averaging Conditional averaging is implemented as a trace post processor that is configured in the "trs" object as follows:

setPostProcessor(trs, CondAvg())

For AES, there are max 256 averages per key byte offset. For DES SBOX out there are 64, and for DES ROUNDOUT there are 1024. This is returned by getNumberOfAverages(). See type CondAvg in conditional-average.jl how the averager works: it uses Julia's newly added Threads module. By default only a single thread is used. If you set the JULIA_NUM_THREADS=2 environment variable it will use 2 threads, making it quite much faster if the input has many samples. Using more than 2 threads doesn't currently speed up the process (on my laptop at least), but I haven't profiled it.

The data passes that are configured with addDataPass determine the data on which conditional averaging operates: for AES this is simply the input or the output. For DES it is not since the input or output data gets bit picked from all over the place before it is recombined with a key, and therefore we cannot simply average on individual input bytes. See function round1 in attackdes-core.jl, for example, to see how data is preprocessed before being fed into the conditional averager.

Parallelization

What's currently parallelized are all the post processors:

  • Conditional bitwise sample reduction
  • Conditional averaging
  • Incremental correlation statistics

All the provided main-xxx.jl files can be run with -pX to run on X processors (local or not, as long as the trace set input is available for all processes).

There are currently 2 different ways to parallelize each post processor:

  • SplitByTracesBlock: This means that each process will visit a subset of traces, but do all the work required for that trace. For conditional averaging this means that each process needs M memory but less trace data needs to be moved around and interpreted. Traces are split in blocks, i.e. the first N go to process 1, the second N to process 2 etc.
  • SplitByTracesSliced: Same as SplitByTracesBLock, but process 1 takes trace 1, process 2 takes trace 2, etc.

Since parallelization is tightly coupled to post processors, you need to pass the SplitXXX instance to the post processor instance, see main-xxx.jl.

Test runs

Since Jlsca is a Julia package, it runs all tests from test/runtests.jl. Either trigger this by:

  1. Pkg.test("Jlsca") in the Julia REPL

  2. julia ../test/runtests.jl from Jlsca's source directory (typically in ~/.julia/v0.5/Jlsca/src)

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