diff options
Diffstat (limited to 'analysis')
| -rw-r--r-- | analysis/countermeasures/collect_leia.ipynb | 349 |
1 files changed, 302 insertions, 47 deletions
diff --git a/analysis/countermeasures/collect_leia.ipynb b/analysis/countermeasures/collect_leia.ipynb index 92d9367..4399c47 100644 --- a/analysis/countermeasures/collect_leia.ipynb +++ b/analysis/countermeasures/collect_leia.ipynb @@ -16,7 +16,7 @@ "outputs": [], "source": [ "from pyecsca.sca.target.ectester import ECTesterTargetLEIA, KeypairEnum, ParameterEnum, CurveEnum, KeyEnum, KeyClassEnum, KeyBuildEnum, KeyAgreementEnum, SignatureEnum, TransformationEnum\n", - "from pyecsca.ec.params import load_params_ectester\n", + "from pyecsca.ec.params import load_params_ectester, get_params\n", "from pyecsca.sca.scope.picoscope_sdk import PS6000Scope\n", "from pyecsca.sca.trace import Trace\n", "from pyecsca.sca.trace.plot import plot_trace, plot_traces\n", @@ -35,6 +35,19 @@ { "cell_type": "code", "execution_count": null, + "id": "3843d504-074a-4736-8c42-114ec63dab7d", + "metadata": {}, + "outputs": [], + "source": [ + "from pyecsca.sca.trace.sampling import downsample_max, downsample_average, downsample_decimate\n", + "from pyecsca.sca.trace.process import rolling_mean, recenter, absolute, threshold\n", + "from pyecsca.sca.trace.filter import filter_lowpass\n", + "from pyecsca.sca.trace.edit import pad, trim" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "865953a5-35ad-473e-a57f-f26368145987", "metadata": {}, "outputs": [], @@ -142,11 +155,18 @@ " scope.setup_capture(channel=\"B\", enable=True)\n", "elif card == \"N2N9\":\n", " # NXP JCOP v2.4.1R3\n", - " actual_freq, n_samples = scope.setup_frequency(frequency=50_000_000, pretrig=0, posttrig=30_000_000)\n", + " # N9\n", + " actual_freq, n_samples = scope.setup_frequency(frequency=625_000_000, pretrig=0, posttrig=500_000_000)\n", " scope.setup_channel(channel=\"A\", coupling=\"DC\", range=1, offset=0, enable=True)\n", " scope.setup_channel(channel=\"B\", coupling=\"DC_50\", range=0.05, offset=-0.280, enable=True)\n", " scope.setup_trigger(channel=\"A\", threshold=0.2, direction=\"rising\", delay=0, timeout=5000, enable=True)\n", " scope.setup_capture(channel=\"B\", enable=True)\n", + " # N2\n", + " #actual_freq, n_samples = scope.setup_frequency(frequency=50_000_000, pretrig=0, posttrig=40_000_000)\n", + " #scope.setup_channel(channel=\"A\", coupling=\"DC\", range=1, offset=0, enable=True)\n", + " #scope.setup_channel(channel=\"B\", coupling=\"DC_50\", range=0.1, offset=-0.18, enable=True)\n", + " #scope.setup_trigger(channel=\"A\", threshold=0.2, direction=\"rising\", delay=0, timeout=5000, enable=True)\n", + " #scope.setup_capture(channel=\"B\", enable=True)\n", "elif card == \"N4\":\n", " # NXP J3H145\n", " # 15M for keygen\n", @@ -203,6 +223,14 @@ ] }, { + "cell_type": "markdown", + "id": "088f806f-63ad-432a-a654-0d8a7d857187", + "metadata": {}, + "source": [ + "## Allocate" + ] + }, + { "cell_type": "code", "execution_count": null, "id": "27c4a7f7-3029-46d9-a3d5-3f4c0b56e37e", @@ -218,6 +246,16 @@ { "cell_type": "code", "execution_count": null, + "id": "5b03d70f-1bf1-4e74-b9e4-b9d1591baee5", + "metadata": {}, + "outputs": [], + "source": [ + "ectester.allocate_sig(SignatureEnum.ALG_ECDSA_SHA)" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "f952cf17-88f2-47d7-88f3-346c6cbd0572", "metadata": {}, "outputs": [], @@ -226,13 +264,22 @@ ] }, { + "cell_type": "markdown", + "id": "74a56989-91e7-44c8-ba9c-4c387c41de86", + "metadata": {}, + "source": [ + "## Set params" + ] + }, + { "cell_type": "code", "execution_count": null, "id": "4d67e5a3-79d3-4160-89c9-07f1cd697f52", "metadata": {}, "outputs": [], "source": [ - "params = load_params_ectester(\"curves/curves_full_order/cofactor256p313_full.csv\", \"affine\")" + "#params = get_params(\"secg\", \"secp256r1\", \"affine\")\n", + "params = load_params_ectester(\"../countermeasures/countermeasures/tests/comb/cofactor256p18446744073709551617_smallgen_fakeorder.csv\", \"affine\")" ] }, { @@ -259,6 +306,14 @@ ] }, { + "cell_type": "markdown", + "id": "7cd21c6a-e741-4e60-98ae-f7f58fa70a02", + "metadata": {}, + "source": [ + "## Generate" + ] + }, + { "cell_type": "code", "execution_count": null, "id": "26be2fb8-607c-4bce-8968-ba57938f9a89", @@ -296,7 +351,7 @@ "metadata": {}, "outputs": [], "source": [ - "trace_gen = scope.retrieve(\"B\", SampleType.Raw)" + "trace_gen = scope.retrieve(\"B\", SampleType.Volt)" ] }, { @@ -312,7 +367,149 @@ { "cell_type": "code", "execution_count": null, - "id": "08978417-6f39-4c40-a862-9ad43179dccc", + "id": "dd2a71ea-02ed-40d9-9268-6b47f304bac1", + "metadata": {}, + "outputs": [], + "source": [ + "plot_trace(downsample_average(trace_gen, 1000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6a835a5-5904-4ec4-891a-ea4b2a57f1c9", + "metadata": {}, + "outputs": [], + "source": [ + "def find_bumps(trace):\n", + " ds = downsample_average(trace, 1000)\n", + " ts = threshold(ds, 0.025)\n", + " prev = 0\n", + " previ = 0\n", + " total = []\n", + " big = []\n", + " for i, sample in enumerate(ts.samples):\n", + " if prev == 0 and sample == 1:\n", + " dist = i - previ\n", + " if dist > 2500 and total:\n", + " big.append(total)\n", + " total = []\n", + " if dist > 500:\n", + " l = [i]\n", + " total.append(l)\n", + " else:\n", + " total[-1].append(i)\n", + " previ = i\n", + " prev = sample\n", + " elif prev == 1 and sample == 0:\n", + " prev = sample\n", + " if total:\n", + " big.append(total)\n", + " s = []\n", + " for t in big:\n", + " seq = []\n", + " for l in t:\n", + " seq.append(str(len(l)))\n", + " s.append(\",\".join(seq))\n", + " return \"-\".join(s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bfb4b6b-f44d-43a4-b52f-fad6f978c301", + "metadata": {}, + "outputs": [], + "source": [ + "find_bumps(trace_gen)" + ] + }, + { + "cell_type": "markdown", + "id": "f795ea2f-c244-435f-b040-9c7e9ef9debd", + "metadata": {}, + "source": [ + "## Or set key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d3daeda-55d4-43d2-b33f-5c9caba41e67", + "metadata": {}, + "outputs": [], + "source": [ + "priv = 0x3c984f3a459a6b8f1a5ece87a695d1b112b978024a9c56c1a12ade3500f29d8c\n", + "pub = params.curve.affine_multiply(params.generator, priv + 5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f12952f-e9fd-44cf-817e-813385306145", + "metadata": {}, + "outputs": [], + "source": [ + "scope.arm()\n", + "sleep(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34457f1e-de06-480e-9668-ef903f6d2b8c", + "metadata": {}, + "outputs": [], + "source": [ + "ectester.set(KeypairEnum.KEYPAIR_LOCAL,\n", + " CurveEnum.external,\n", + " ParameterEnum.S | ParameterEnum.W,\n", + " {**ECTesterTargetLEIA.encode_parameters(ParameterEnum.S, priv),\n", + " **ECTesterTargetLEIA.encode_parameters(ParameterEnum.W, pub)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5117598-8cee-4af8-89f1-27af2360bd96", + "metadata": {}, + "outputs": [], + "source": [ + "scope.capture(10000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8b68498-359a-47bd-ac20-c298b933cda7", + "metadata": {}, + "outputs": [], + "source": [ + "trace_set = scope.retrieve(\"B\", SampleType.Volt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b2f0bb8-87a2-400a-90b3-b47b4181721b", + "metadata": {}, + "outputs": [], + "source": [ + "plot_trace(trace_set)" + ] + }, + { + "cell_type": "markdown", + "id": "2037aa6a-3533-4f20-b618-154aa040892c", + "metadata": {}, + "source": [ + "## ECDSA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2af07d2a-d005-4a75-9a71-f2cd0e723335", "metadata": {}, "outputs": [], "source": [ @@ -323,23 +520,70 @@ { "cell_type": "code", "execution_count": null, - "id": "f4c1df63-4da3-4693-9aaa-217e4a9314f5", + "id": "c49b3f74-6229-463d-a4eb-468d7d98d4e4", + "metadata": {}, + "outputs": [], + "source": [ + "resp = ectester.ecdsa_sign(KeypairEnum.KEYPAIR_LOCAL,\n", + " True,\n", + " SignatureEnum.ALG_ECDSA_SHA,\n", + " b\"message\")\n", + "resp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5078978-5867-438b-a4e1-7e4cc35d32e6", + "metadata": {}, + "outputs": [], + "source": [ + "scope.capture(10000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b7d93be-78df-44a5-ae04-5db99db5a099", + "metadata": {}, + "outputs": [], + "source": [ + "trace_ecdsa = scope.retrieve(\"B\", SampleType.Volt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0abe66cf-b10f-49b8-a525-e606136303de", + "metadata": {}, + "outputs": [], + "source": [ + "plot_trace(trace_ecdsa)" + ] + }, + { + "cell_type": "markdown", + "id": "78f9c17b-c195-4c70-a5b7-79337de3112a", + "metadata": {}, + "source": [ + "## ECDH" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08978417-6f39-4c40-a862-9ad43179dccc", "metadata": {}, "outputs": [], "source": [ - "with open(\"curves/cofactor_points/point_313.csv\", \"r\") as f:\n", - " line = f.read()\n", - " sx, sy = line.split(\",\")\n", - " bx = bytes.fromhex(sx[2:])\n", - " by = bytes.fromhex(sy[2:])\n", - " point = bytes([0x04]) + bx + by\n", - " print(point.hex())" + "scope.arm()\n", + "sleep(2)" ] }, { "cell_type": "code", "execution_count": null, - "id": "ad17301a-902f-48e9-936e-f553a1a8f006", + "id": "d7626f39-53e4-41a7-afe2-d89e15f6a0c9", "metadata": {}, "outputs": [], "source": [ @@ -347,7 +591,7 @@ " True,\n", " TransformationEnum.NONE,\n", " KeyAgreementEnum.ALG_EC_SVDP_DH,\n", - " point) # pubkey as bytes" + " bytes(params.generator))" ] }, { @@ -381,131 +625,142 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "6c46be4f-8901-4b29-a30e-71ac28bafcd9", + "cell_type": "markdown", + "id": "309e8d83-771b-4783-9bb5-474c9d5bf8ec", "metadata": {}, - "outputs": [], "source": [ - "from pyecsca.sca.trace.edit import pad, trim" + "## Misc" ] }, { "cell_type": "code", "execution_count": null, - "id": "1d4b775e-fa7d-48aa-bb24-bec7450c8114", + "id": "a29e460b-cca7-4d28-b2e2-3920b0870628", "metadata": {}, "outputs": [], "source": [ - "trim?" + "ectester.cleanup()" ] }, { "cell_type": "code", "execution_count": null, - "id": "7164dab5-090f-4a6a-9ce6-8e252e71af76", + "id": "264b4653-023c-4a39-8970-270c2f0d42c6", "metadata": {}, "outputs": [], "source": [ - "plot_traces(ecdh_ok, pad(trim(trace_ecdh, 0, len(trace_ecdh) -363700), (363700, 0)))" + "ectester.disconnect()" ] }, { "cell_type": "code", "execution_count": null, - "id": "0395b2aa-3c00-4068-8179-039e7e5ad039", + "id": "1d706782-7124-4879-8198-407e45f131ff", "metadata": {}, "outputs": [], "source": [ - "from scipy import signal\n", - "import numpy as np" + "scope.close()" + ] + }, + { + "cell_type": "markdown", + "id": "a5ed4ed6-7732-41a6-b6a9-61e76851f468", + "metadata": {}, + "source": [ + "### Frequency analysis" ] }, { "cell_type": "code", "execution_count": null, - "id": "1b63e6d3-8488-4c13-91fa-388318e8e1bc", + "id": "60139124-f51c-4e1c-858c-e46731d38bde", "metadata": {}, "outputs": [], "source": [ - "corr = signal.correlate(trace_ecdh.samples, trace_ecdh.samples, mode=\"full\")\n", - "lags = signal.correlation_lags(trace_ecdh.samples.size, trace_ecdh.samples.size, mode=\"full\")\n", - "lag = lags[np.argmax(corr)]\n" + "plot_trace(filter_lowpass(trim(trace_ecdsa, 55_000_000, 56_000_000), 625000000, 250_000_000))" ] }, { "cell_type": "code", "execution_count": null, - "id": "f6fbd522-c9db-4dbc-bd05-109ba3d3fdf9", + "id": "031d0c18-7408-46e0-8853-cc9e485902d1", "metadata": {}, "outputs": [], "source": [ - "plot_trace(Trace(corr))" + "from scipy.signal import periodogram\n", + "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, - "id": "5a910cc9-80bd-4c74-a36a-56804aea42e1", + "id": "40be47b6-b08a-49e2-b40e-2b5b6ca6be13", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "%matplotlib widget" + ] }, { "cell_type": "code", "execution_count": null, - "id": "195ed167-8925-44c9-b0fb-6e207f8fdf8a", + "id": "65aa1eb2-2694-4697-a2cd-869272218b6a", "metadata": {}, "outputs": [], "source": [ - "ectester.cleanup()" + "f, Pxx_den = periodogram(recenter(trim(trace_ecdsa, 30_000_000, 500_000_000)).samples, 625000000)" ] }, { "cell_type": "code", "execution_count": null, - "id": "264b4653-023c-4a39-8970-270c2f0d42c6", + "id": "c52d798b-d40b-4914-908a-3b34c51a2084", "metadata": {}, "outputs": [], "source": [ - "ectester.disconnect()" + "plt.semilogy(f, Pxx_den)\n", + "#plt.ylim([1e-7, 1e2])\n", + "plt.xlabel('frequency [Hz]')\n", + "plt.ylabel('PSD [V**2/Hz]')\n", + "plt.show()" ] }, { "cell_type": "code", "execution_count": null, - "id": "1d706782-7124-4879-8198-407e45f131ff", + "id": "197fd1b9-7d91-4f23-b89d-e68727be36d7", "metadata": {}, "outputs": [], "source": [ - "scope.close()" + "from scipy.signal import find_peaks" ] }, { "cell_type": "code", "execution_count": null, - "id": "768a8fe0-27ec-4678-a66a-66e7680fd6c8", + "id": "1fd46ee6-de5e-45f6-bee0-e3ead0029aca", "metadata": {}, "outputs": [], "source": [ - "from pyecsca.sca.trace.process import rolling_mean, recenter\n", - "from pyecsca.sca.trace.filter import filter_lowpass" + "pks = find_peaks(Pxx_den, height=1.52e-7)" ] }, { "cell_type": "code", "execution_count": null, - "id": "d93ce9cd-1516-4e32-9fed-94a19fac23f4", + "id": "3b03f991-7f89-43df-9726-13515ae78c26", "metadata": {}, "outputs": [], "source": [ - "from pyecsca.sca.trace.plot import plot_trace, plot_traces" + "len(pks[0])\n", + "for pk in f[pks[0]]:\n", + " print(int(pk))" ] }, { "cell_type": "code", "execution_count": null, - "id": "45f2a8ab-af15-46f9-904e-76e5eeb08978", + "id": "d4f527fe-96f3-4e90-b802-3d063beb619c", "metadata": {}, "outputs": [], "source": [] |