Friday, November 3, 2017

Ok, I'll try to resume the tools fro breaking chaos modulation, starting with spicy

# Author: Travis Oliphant
# 1999 -- 2002
from __future__ import division, print_function, absolute_import
import operator
import threading
import sys
import timeit
from . import sigtools, dlti
from ._upfirdn import upfirdn, _output_len
from scipy._lib.six import callable
from scipy._lib._version import NumpyVersion
from scipy import fftpack, linalg
from numpy import (allclose, angle, arange, argsort, array, asarray,
atleast_1d, atleast_2d, cast, dot, exp, expand_dims,
iscomplexobj, mean, ndarray, newaxis, ones, pi,
poly, polyadd, polyder, polydiv, polymul, polysub, polyval,
product, r_, ravel, real_if_close, reshape,
roots, sort, take, transpose, unique, where, zeros,
zeros_like)
import numpy as np
import math
from scipy.special import factorial
from .windows import get_window
from ._arraytools import axis_slice, axis_reverse, odd_ext, even_ext, const_ext
from .filter_design import cheby1, _validate_sos
from .fir_filter_design import firwin
if sys.version_info.major >= 3 and sys.version_info.minor >= 5:
from math import gcd
else:
from fractions import gcd
__all__ = ['correlate', 'fftconvolve', 'convolve', 'convolve2d', 'correlate2d',
'order_filter', 'medfilt', 'medfilt2d', 'wiener', 'lfilter',
'lfiltic', 'sosfilt', 'deconvolve', 'hilbert', 'hilbert2',
'cmplx_sort', 'unique_roots', 'invres', 'invresz', 'residue',
'residuez', 'resample', 'resample_poly', 'detrend',
'lfilter_zi', 'sosfilt_zi', 'sosfiltfilt', 'choose_conv_method',
'filtfilt', 'decimate', 'vectorstrength']
_modedict = {'valid': 0, 'same': 1, 'full': 2}
_boundarydict = {'fill': 0, 'pad': 0, 'wrap': 2, 'circular': 2, 'symm': 1,
'symmetric': 1, 'reflect': 4}
_rfft_mt_safe = (NumpyVersion(np.__version__) >= '1.9.0.dev-e24486e')
_rfft_lock = threading.Lock()
def _valfrommode(mode):
try:
val = _modedict[mode]
except KeyError:
if mode not in [0, 1, 2]:
raise ValueError("Acceptable mode flags are 'valid' (0),"
" 'same' (1), or 'full' (2).")
val = mode
return val
def _bvalfromboundary(boundary):
try:
val = _boundarydict[boundary] << 2
except KeyError:
if val not in [0, 1, 2]:
raise ValueError("Acceptable boundary flags are 'fill', 'wrap'"
" (or 'circular'), \n and 'symm'"
" (or 'symmetric').")
val = boundary << 2
return val
def _inputs_swap_needed(mode, shape1, shape2):
"""
If in 'valid' mode, returns whether or not the input arrays need to be
swapped depending on whether `shape1` is at least as large as `shape2` in
every dimension.
This is important for some of the correlation and convolution
implementations in this module, where the larger array input needs to come
before the smaller array input when operating in this mode.
Note that if the mode provided is not 'valid', False is immediately
returned.
"""
if mode == 'valid':
ok1, ok2 = True, True
for d1, d2 in zip(shape1, shape2):
if not d1 >= d2:
ok1 = False
if not d2 >= d1:
ok2 = False
if not (ok1 or ok2):
raise ValueError("For 'valid' mode, one must be at least "
"as large as the other in every dimension")
return not ok1
return False
def correlate(in1, in2, mode='full', method='auto'):
r"""
Cross-correlate two N-dimensional arrays.
Cross-correlate `in1` and `in2`, with the output size determined by the
`mode` argument.
Parameters
----------
in1 : array_like
First input.
in2 : array_like
Second input. Should have the same number of dimensions as `in1`.
mode : str {'full', 'valid', 'same'}, optional
A string indicating the size of the output:
``full``
The output is the full discrete linear cross-correlation
of the inputs. (Default)
``valid``
The output consists only of those elements that do not
rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
must be at least as large as the other in every dimension.
``same``
The output is the same size as `in1`, centered
with respect to the 'full' output.
method : str {'auto', 'direct', 'fft'}, optional
A string indicating which method to use to calculate the correlation.
``direct``
The correlation is determined directly from sums, the definition of
correlation.
``fft``
The Fast Fourier Transform is used to perform the correlation more
quickly (only available for numerical arrays.)
``auto``
Automatically chooses direct or Fourier method based on an estimate
of which is faster (default). See `convolve` Notes for more detail.
.. versionadded:: 0.19.0
Returns
-------
correlate : array
An N-dimensional array containing a subset of the discrete linear
cross-correlation of `in1` with `in2`.
See Also
--------
choose_conv_method : contains more documentation on `method`.
Notes
-----
The correlation z of two d-dimensional arrays x and y is defined as::
z[...,k,...] = sum[..., i_l, ...] x[..., i_l,...] * conj(y[..., i_l - k,...])
This way, if x and y are 1-D arrays and ``z = correlate(x, y, 'full')`` then
.. math::
z[k] = (x * y)(k - N + 1)
= \sum_{l=0}^{||x||-1}x_l y_{l-k+N-1}^{*}
for :math:`k = 0, 1, ..., ||x|| + ||y|| - 2`
where :math:`||x||` is the length of ``x``, :math:`N = \max(||x||,||y||)`,
and :math:`y_m` is 0 when m is outside the range of y.
``method='fft'`` only works for numerical arrays as it relies on
`fftconvolve`. In certain cases (i.e., arrays of objects or when
rounding integers can lose precision), ``method='direct'`` is always used.
Examples
--------
Implement a matched filter using cross-correlation, to recover a signal
that has passed through a noisy channel.
>>> from scipy import signal
>>> sig = np.repeat([0., 1., 1., 0., 1., 0., 0., 1.], 128)
>>> sig_noise = sig + np.random.randn(len(sig))
>>> corr = signal.correlate(sig_noise, np.ones(128), mode='same') / 128
>>> import matplotlib.pyplot as plt
>>> clock = np.arange(64, len(sig), 128)
>>> fig, (ax_orig, ax_noise, ax_corr) = plt.subplots(3, 1, sharex=True)
>>> ax_orig.plot(sig)
>>> ax_orig.plot(clock, sig[clock], 'ro')
>>> ax_orig.set_title('Original signal')
>>> ax_noise.plot(sig_noise)
>>> ax_noise.set_title('Signal with noise')
>>> ax_corr.plot(corr)
>>> ax_corr.plot(clock, corr[clock], 'ro')
>>> ax_corr.axhline(0.5, ls=':')
>>> ax_corr.set_title('Cross-correlated with rectangular pulse')
>>> ax_orig.margins(0, 0.1)
>>> fig.tight_layout()
>>> fig.show()
"""
in1 = asarray(in1)
in2 = asarray(in2)
if in1.ndim == in2.ndim == 0:
return in1 * in2
elif in1.ndim != in2.ndim:
raise ValueError("in1 and in2 should have the same dimensionality")
# Don't use _valfrommode, since correlate should not accept numeric modes
try:
val = _modedict[mode]
except KeyError:
raise ValueError("Acceptable mode flags are 'valid',"
" 'same', or 'full'.")
# this either calls fftconvolve or this function with method=='direct'
if method in ('fft', 'auto'):
return convolve(in1, _reverse_and_conj(in2), mode, method)
# fastpath to faster numpy.correlate for 1d inputs when possible
if _np_conv_ok(in1, in2, mode):
return np.correlate(in1, in2, mode)
# _correlateND is far slower when in2.size > in1.size, so swap them
# and then undo the effect afterward if mode == 'full'. Also, it fails
# with 'valid' mode if in2 is larger than in1, so swap those, too.
# Don't swap inputs for 'same' mode, since shape of in1 matters.
swapped_inputs = ((mode == 'full') and (in2.size > in1.size) or
_inputs_swap_needed(mode, in1.shape, in2.shape))
if swapped_inputs:
in1, in2 = in2, in1
if mode == 'valid':
ps = [i - j + 1 for i, j in zip(in1.shape, in2.shape)]
out = np.empty(ps, in1.dtype)
z = sigtools._correlateND(in1, in2, out, val)
else:
ps = [i + j - 1 for i, j in zip(in1.shape, in2.shape)]
# zero pad input
in1zpadded = np.zeros(ps, in1.dtype)
sc = [slice(0, i) for i in in1.shape]
in1zpadded[sc] = in1.copy()
if mode == 'full':
out = np.empty(ps, in1.dtype)
elif mode == 'same':
out = np.empty(in1.shape, in1.dtype)
z = sigtools._correlateND(in1zpadded, in2, out, val)
if swapped_inputs:
# Reverse and conjugate to undo the effect of swapping inputs
z = _reverse_and_conj(z)
return z
def _centered(arr, newshape):
# Return the center newshape portion of the array.
newshape = asarray(newshape)
currshape = array(arr.shape)
startind = (currshape - newshape) // 2
endind = startind + newshape
myslice = [slice(startind[k], endind[k]) for k in range(len(endind))]
return arr[tuple(myslice)]

https://github.com/scipy/scipy/blob/master/scipy/signal/signaltools.py
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Information Leakage from Optical Emanations (JOE LOUGHRY Lockheed Martin Space Systems and DAVID A. UMPHRESS Auburn University)

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Eavesdropping attacks on computer displays

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Russian Su-27 Flanker Intercepts NATO Portuguese P-3 Orion over the Balt...

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Thursday, November 2, 2017

Ok....no bullshit...no easy stuff...on the bootloader

Finding the Encryption Key

Now that we have our traces, we can go ahead and perform the attack. As described in the background theory, we'll have to do two attacks - one to get the 14th round key, and another (using the first result) to get the 13th round key. Then, we'll do some post-processing to finally get the 256 bit encryption key.

14th Round Key

We can attack the 14th round key with a standard, no-frills CPA attack:
  1. Open the ChipWhisperer Analyzer program and load the .cwp file with the 13th and 14th round traces. This can be either the aes256_round1413_key0_100.cwp file downloaded or the capture you performed.
  2. View and manipulate the trace data with the following steps:
    1. Switch to the Trace Output Plot tab
    2. Switch to the Results parameter setting tab
    3. Choose the traces to be plotted and press the Redraw button to draw them
    4. Right-click on the waveform to change options, or left-click and drag to zoom
    5. Use the toolbar to quickly reset the zoom back to original
      image
      Notice that the traces are synchronized for the first 7000 samples, but become unsynchronized later. This fact will be important later in the tutorial.
  3. Set up the attack in the Attack settings tab:
    1. Leave the Crypto Algorithm set to AES-128. (Remember that we're applying the AES-128 attack to half of the AES-256 key!)
    2. Change the Leakage Model to HW: AES Inv SBox Output, First Round (Dec).
    3. If you're finding the attack very slow, narrow down the attack a bit. Normally, this requires a bit of investigation to determine which ranges of the trace are important. Here, you can use the range from 2900 for 4200. The default settings will also work fine!
      image
  4. Note that we do not know the secret encryption key, so we cannot highlight the correct key automatically. If you want to fix this, the Results settings tab has a Highlighted Key setting. Change this to Override mode and enter the key ea 79 79 20 c8 71 44 7d 46 62 5f 51 85 c1 3b cb.
  5. Finally, run the attack by switching to the Results Table tab and then hitting the Attack button.

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we have the russians intel work about NSA! Godsurge

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the closest i can get is for software is...silentbreaksec/Throwback...HTTP/S Beaconing Implant

Throwback

HTTP/S Beaconing Implant
  1. Run the python script to encode strings. python tbManger.py encode http://mydomain.com/index.php
http://mydomain.com/index.php -> {57,37,37,33,107,126,126,60,40,53,62,60,48,56,63,127,50,62,60,126,56,63,53,52,41,127,33,57,33}
Note: Don't forget to add ,-1 to end of the integer array for an LP. So the above would become.
{57,37,37,33,107,126,126,60,40,53,62,60,48,56,63,127,50,62,60,126,56,63,53,52,41,127,33,57,33,-1}
  1. Update DNSARRAY to reflect the number of LPs listed in DNSCODE array.
  2. Compile!
  3. Setup ThrowbackLP.
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Godsurge is a physical device plugged-in to the Joint Test Action Group or JTAG headers on a system's motherboard. ....NSA implants...watchdog running off an integrated RC oscillator, presenting Microchip AT91SAM7X128C-AU, 32bit ARM Microcontroller, 30MHz, 128 kB Flash, 100-Pin LQFP

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EchoEra Corp Supremacy electronic interface