Protecting Django with a Deep Neural Network

This guide is a walkthrough for preparing and training a deep neural network for defending Django application servers. The accuracy is currently 70% without tuning the DNN or adding in actual exploits or sql-injection attacks into the attack datasets.

In the future I am looking to extend the full datasets to include the TCP payload data stream (hex bytes) for sentiment analysis using an embedding Keras layer (https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html). I imagine deserialized payloads will only increase the default accuracy, but it is only an assumption for now.

Setup

  1. Run these commands to clone the repositories to the same directories for making debugging easier for all users.

    mkdir -p -m 777 /opt/antinex
git clone https://github.com/jay-johnson/train-ai-with-django-swagger-jwt.git /opt/antinex/api
git clone https://github.com/jay-johnson/network-pipeline-datasets.git /opt/antinex/datasets
git clone https://github.com/jay-johnson/antinex-datasets.git /opt/antinex/antinex-datasets

  2. Start the REST API

    If the REST API is not running, please start it in a new terminal so it can process the prepare and training requests.

    cd /opt/antinex/api
source ~/.venvs/venvdrfpipeline/bin/activate
./install.sh
./start.sh

(Optional) Prepare Attack Dataset

If you want to prepare your own attack dataset run these commands with the REST API running locally:

source ~/.venvs/venvdrfpipeline/bin/activate
export TEST_DATA=/opt/antinex/antinex-datasets/v1/webapps/django/configs/django-attack-prepare-v1.json
/opt/antinex/api/tests/build-new-dataset.py

Check the files were updated:

ls -l /opt/antinex/antinex-datasets/v1/webapps/django/inputs/attack/
total 5088
-rw-rw-r-- 1 jay jay    2144 Feb 15 09:22 cleaned_v1_django_attack_metadata.json
-rw-rw-r-- 1 jay jay    2455 Feb 15 09:22 fulldata_v1_django_attack_metadata.json
-rw-rw-r-- 1 jay jay 1131875 Feb 15 09:22 v1_django_cleaned_attack.csv
-rw-rw-r-- 1 jay jay 4064695 Feb 15 09:22 v1_django_full_attack.csv

(Optional) Prepare Full Dataset

If you want to prepare your own full dataset run these commands with the REST API running locally:

source ~/.venvs/venvdrfpipeline/bin/activate
export TEST_DATA=/opt/antinex/antinex-datasets/v1/webapps/django/configs/django-prepare-v1.json
/opt/antinex/api/tests/build-new-dataset.py

Confirm Dataset is Ready

/opt/antinex/antinex-datasets/tools/describe-v1-training.py /opt/antinex/antinex-datasets/v1/webapps/django/training-ready/v1_django_cleaned.csv

Hopefully your dataset has both attack and non-attack records like:

2018-02-15 09:23:23,963 - describe-training-data - INFO - total records=30200 attack=9000 nonattack=21200 percent_attack=29.80% percent_nonattack=70.20%

What you don’t want to see is this in the output:

2018-02-15 08:47:41,389 - describe-training-data - INFO - total records=21200 attack=0 nonattack=21200 percent_attack=0.00% percent_nonattack=100.00%

That means the prepare step failed to add the attack data into the dataset correctly. Please go back to the Prepare Dataset step and review paths to the files are correct.

Train Dataset

source ~/.venvs/venvdrfpipeline/bin/activate
export TEST_DATA=/opt/antinex/antinex-datasets/v1/webapps/django/configs/django-train-v1.json
/opt/antinex/api/tests/create-keras-dnn.py

From the logs taken during creation of this doc, the model is 70% accurate at predicting attack records.

/opt/antinex/api/tests/create-keras-dnn.py INFO:create-keras-dnn:Logging in user url=http://localhost:8010/api-token-auth/ INFO:create-keras-dnn:logged in user=root token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJ1c2VyX2lkIjoxLCJ1c2VybmFtZSI6InJvb3QiLCJleHAiOjE1MTg3MTYwMzksImVtYWlsIjoicm9vdEBlbWFpbC5jb20ifQ.nvFa1_GS4G4A7yLz4zaX5pf_TLrTxfRwE4AYf5l-32Y INFO:create-keras-dnn:building post data INFO:create-keras-dnn:Running ML Job url=http://localhost:8010/ml/ test_data={‘csv_file’: ‘/opt/antinex/antinex-datasets/v1/webapps/django/training-ready/v1_django_cleaned.csv’, ‘meta_file’: ‘/opt/antinex/antinex-datasets/v1/webapps/django/training-ready/cleaned_v1_django_metadata.json’, ‘title’: ‘Django - Keras DNN - Dataset v1’, ‘desc’: ‘Training Django DNN using Attack and Non-attack data captured using the network-pipeline’, ‘ds_name’: ‘cleaned’, ‘algo_name’: ‘dnn’, ‘ml_type’: ‘keras’, ‘predict_feature’: ‘label_value’, ‘training_data’: ‘{}’, ‘pre_proc’: ‘{}’, ‘post_proc’: ‘{}’, ‘meta_data’: ‘{}’, ‘version’: 1} INFO:create-keras-dnn:SUCCESS - Post Response status=201 reason=Created INFO:create-keras-dnn:{‘job’: {‘id’: 14, ‘user_id’: 1, ‘user_name’: ‘root’, ‘title’: ‘Django - Keras DNN - Dataset v1’, ‘desc’: ‘Training Django DNN using Attack and Non-attack data captured using the network-pipeline’, ‘ds_name’: ‘cleaned’, ‘algo_name’: ‘dnn’, ‘ml_type’: ‘keras’, ‘status’: ‘initial’, ‘control_state’: ‘active’, ‘predict_feature’: ‘label_value’, ‘training_data’: {}, ‘pre_proc’: {}, ‘post_proc’: {}, ‘meta_data’: {}, ‘tracking_id’: ‘ml_befd247b-7163-4909-87d3-7e43189471a3’, ‘version’: 1, ‘created’: ‘2018-02-15 17:28:59’, ‘updated’: ‘2018-02-15 17:28:59’, ‘deleted’: ‘’}, ‘results’: {‘id’: 10, ‘user_id’: 1, ‘user_name’: ‘root’, ‘job_id’: 14, ‘status’: ‘finished’, ‘version’: 1, ‘acc_data’: {‘accuracy’: 70.9602648927676}, ‘error_data’: None, ‘model_json’: ‘{“class_name”: “Sequential”, “config”: [{“class_name”: “Dense”, “config”: {“name”: “dense_1”, “trainable”: true, “batch_input_shape”: [null, 68], “dtype”: “float32”, “units”: 8, “activation”: “relu”, “use_bias”: true, “kernel_initializer”: {“class_name”: “RandomUniform”, “config”: {“minval”: -0.05, “maxval”: 0.05, “seed”: null}}, “bias_initializer”: {“class_name”: “Zeros”, “config”: {}}, “kernel_regularizer”: null, “bias_regularizer”: null, “activity_regularizer”: null, “kernel_constraint”: null, “bias_constraint”: null}}, {“class_name”: “Dense”, “config”: {“name”: “dense_2”, “trainable”: true, “units”: 6, “activation”: “relu”, “use_bias”: true, “kernel_initializer”: {“class_name”: “RandomUniform”, “config”: {“minval”: -0.05, “maxval”: 0.05, “seed”: null}}, “bias_initializer”: {“class_name”: “Zeros”, “config”: {}}, “kernel_regularizer”: null, “bias_regularizer”: null, “activity_regularizer”: null, “kernel_constraint”: null, “bias_constraint”: null}}, {“class_name”: “Dense”, “config”: {“name”: “dense_3”, “trainable”: true, “units”: 1, “activation”: “sigmoid”, “use_bias”: true, “kernel_initializer”: {“class_name”: “RandomUniform”, “config”: {“minval”: -0.05, “maxval”: 0.05, “seed”: null}}, “bias_initializer”: {“class_name”: “Zeros”, “config”: {}}, “kernel_regularizer”: null, “bias_regularizer”: null, “activity_regularizer”: null, “kernel_constraint”: null, “bias_constraint”: null}}], “keras_version”: “2.1.4”, “backend”: “tensorflow”}’, ‘model_weights’: {‘weights’: ‘[[[0.1209325939,0.125004366,0.04392628,0.0058005759,0.0283837207,0.1084360257,-0.0221137945,0.1256226152],[0.0659536794,0.0419033654,0.0170708448,-0.0357947014,0.0545291603,0.0852750689,-0.0538051911,0.0699182898],[-0.1545251906,0.1111956462,-0.0147213368,0.0731624961,0.0864041299,-0.0017828628,-0.0178274736,0.1216073707],[0.1336804777,0.1023893878,0.0170907527,0.0518929921,0.0966836065,0.1597969532,0.0217716675,0.085526742],[0.1376502961,0.0593057014,0.0694345012,-0.0378300808,-0.0057742135,0.0791756362,0.0235338192,0.0824202821],[0.1233218759,0.0594531633,-0.0086939791,-0.0387902856,-0.0360716954,0.0850752816,-0.0114007629,0.0921288431],[-0.1866215467,0.0660640672,-0.0012092546,0.0663910806,0.0470964499,-0.0053454894,-0.0322002359,0.1119380593],[-0.2092835754,0.0618410148,0.0634177029,0.0136457058,0.0184208602,-0.0168401636,-0.0061000162,0.0907672495],[-0.1857144684,0.1216576323,0.0256078299,0.0622631498,0.0746099502,-0.0211249851,-0.058073774,0.0694563985],[-0.1634339541,0.0696025267,0.0837683603,0.092234768,0.0956235006,-0.0282980427,-0.040868368,0.113351725],[-0.1704672724,0.0691201314,0.071548,0.069260262,0.1131449491,-0.0108540468,-0.0425567217,0.0640222654],[-0.2344770879,0.0428572707,0.0793405771,0.0114616835,0.0313309282,0.0125392843,0.0246058684,0.0830053911],[-0.2056925297,0.0455387048,0.0595687404,0.0481434427,0.0567863956,-0.0198212601,0.0394698866,0.1153029948],[-0.1900539547,0.0648706928,0.0755348429,0.0531642176,0.0225196443,-0.037876796,-0.050964918,0.1065156162],[-0.1578101218,0.0851738676,0.080335848,0.1033686772,0.0672588199,-0.0466286503,0.0274061691,0.1454075873],[-0.1649408489,0.1129061654,0.0413498543,0.0406241789,0.0480666906,-0.0626311898,-0.0147714363,0.1095488593],[-0.1902400851,0.0546266772,-0.0002129045,0.081249468,0.0261988528,-0.0456862338,0.033373639,0.1446813047],[-0.2027439624,0.1166701987,0.0509505421,0.0388729684,0.0726077035,-0.0771305785,0.0307749175,0.0881667733],[-0.183378011,0.0356091969,0.0145586552,0.0244816709,0.0284536369,-0.0071689375,-0.0315265507,0.1486145407],[-0.1512038261,0.0764901713,0.061774157,0.0451634862,0.062191762,-0.0525121838,0.0302008335,0.0902092978],[-0.2147508562,0.1103100851,0.017692728,0.0237773284,0.051074788,-0.0431679115,0.0140604237,0.0951612145],[-0.1971089989,0.0693886876,0.0808551386,0.0206196532,0.0207697973,-0.039527256,-0.0105302734,0.0831609368],[-0.2245272845,0.1265042126,0.0620332398,0.0791124329,0.0703641251,-0.0618047714,-0.0481486395,0.1099268496],[-0.1919761449,0.1043844149,0.028273426,0.0244386699,0.0346066169,-0.0108972676,-0.0562972091,0.1470609605],[-0.1688121408,0.082510218,0.0394482091,0.0875071362,0.1120331734,-0.0286579132,-0.0526521802,0.0697672367],[-0.1516744047,0.0463477261,0.0772118047,0.0861110315,0.0297314562,-0.0423166379,-0.006591965,0.0826482922],[-0.2255488038,0.1261228472,0.0011998075,0.0771774799,0.0211141836,-0.0571117215,0.0122114196,0.1291222423],[-0.2126398236,0.1168462709,0.0215776451,0.0146405725,0.1047314554,-0.0019828572,-0.000542541,0.0922133848],[0.2239516675,-0.1272274554,-0.0545392334,-0.0690253899,-0.0240141228,0.0290964283,-0.0048073386,-0.1407266706],[-0.1601528823,0.0873816535,0.0222081486,0.0657906458,0.0444654338,-0.0557228811,0.0258848574,0.1467054784],[0.1103282571,0.062621966,-0.0022228661,0.0238575842,0.0446507819,0.0041218554,-0.009616375,0.0077553303],[-0.190007925,0.0701109022,-0.0119564654,0.0722162873,0.0221073441,-0.0255252719,-0.0031145217,0.1183288619],[-0.2136628032,0.0534639172,0.0714060888,0.0613537244,0.0842673779,-0.0301567074,0.0307384171,0.0729675815],[-0.1955242753,0.0495460741,0.0524940416,0.0604530908,0.0595010929,0.0166093316,0.0391650833,0.0827598944],[-0.0501433276,0.1038881019,0.0417513065,0.0355237499,0.0351028442,-0.0003712648,-0.0190300811,0.0249827243],[-0.1487564296,0.1013090238,0.0214297064,0.0933733582,0.0616822541,-0.0710285828,-0.0045771608,0.1217406169],[-0.1543573439,0.0395659693,0.030846579,0.0086379368,0.0861991048,-0.048596736,-0.0271895695,0.1182741746],[-0.1961124837,0.0571020655,0.0076962849,0.0844913498,0.0193723273,0.0068046544,0.0302888621,0.143293947],[-0.0618059374,0.1257505566,0.0142706381,0.1050991416,0.0157455113,-0.0454091579,-0.0202189703,0.0501390435],[-0.2188960463,0.0440853648,0.07568717,0.0457809009,0.0491918027,-0.0327213667,-0.0431831405,0.1276204884],[0.1394346952,-0.088656649,-0.0315131843,-0.0401459411,-0.0437365025,0.0486980379,0.0174318217,-0.0507184826],[-0.1698808372,0.0357401222,0.0317347683,0.0144515438,0.0194635,0.0180209279,-0.0143435514,0.0795696303],[-0.06071667,-0.0370089039,-0.0037398755,0.0016650554,-0.0761372149,-0.0823373273,-0.0147782043,-0.0841278732],[0.0316180326,-0.0783421397,0.000754284,-0.0383572131,-0.0244521741,-0.0295800623,-0.0005746271,-0.0404013805],[-0.0592732318,-0.0370828509,0.0262605324,0.0704324692,0.006007982,-0.117112644,-0.0359724984,0.011283231],[-0.0184435453,-0.0733112022,-0.0283645988,0.0459322594,-0.0326308981,-0.0342220962,0.0310261045,-0.1323656887],[-0.0456168354,0.0782427266,0.057690192,0.0134688364,0.0762039647,-0.015021774,-0.0588690341,0.1178085133],[-0.2152218074,0.0512532629,-0.0107616447,0.0344684459,0.0744112581,0.0065470482,-0.0018064472,0.1076126173],[-0.1873236597,0.1053858474,-0.011723455,0.058383096,0.1098771915,-0.0099292547,-0.0258859675,0.0824473128],[-0.1549893022,0.0844767764,0.0727496445,0.0866250396,0.0313318856,-0.0712586939,0.011944362,0.1336840689],[-0.2329286635,0.1031851396,0.0821548253,0.0666918531,0.0968727544,-0.0197768845,0.0252549183,0.0636012107],[-0.216961056,0.0476008765,0.041463919,0.0264160633,0.0959576741,-0.0256117992,-0.0261161607,0.0812392458],[-0.1800720841,0.0473946743,0.0632657334,0.0181837026,0.0440427177,-0.0560101122,-0.0301355477,0.1441762149],[-0.0096860044,-0.0820260122,-0.0727077052,-0.0164042395,-0.0778253227,-0.0372423194,-0.2164034247,-0.0453402027],[-0.2012402564,0.0422893129,0.0804941952,0.09306252,0.1128023267,-0.0451305024,-0.0048267297,0.0649067611],[-0.2305155843,0.0032782811,-0.0235605519,-0.0537962541,-0.0118097244,-0.0141736846,0.1753456295,0.0044183824],[-0.2079010457,0.069323428,0.0517042466,0.0940786377,-0.0039390367,-0.0135685029,-0.0057561048,0.0483344011],[-0.179656595,0.0572441481,0.0705757067,0.0079024527,0.0245193392,-0.0251601636,0.0155755458,0.0752719566],[-0.1713959128,0.1208590493,0.0346451364,0.0616195463,0.0427664891,0.0182297006,-0.0133397086,0.1184593365],[-0.2043197453,0.0445172973,0.0081551857,0.092943117,0.0198938642,-0.0723016635,0.0256206822,0.0992775932],[-0.2159956694,0.0412812345,0.0545012057,0.0114885671,0.0877385288,-0.0408159904,0.008009661,0.0881702825],[-0.1577915996,0.0643116087,0.0129699642,0.028255403,0.1068808511,-0.0763266757,0.0346705541,0.0588488467],[-0.2040084004,0.0085537154,-0.0356605425,-0.0487324521,0.0015256398,-0.0052802409,0.1076657921,-0.0199613888],[-0.2131965607,-0.0604146346,-0.0845651999,0.0071733561,-0.0342927612,-0.0888390467,-0.0313468054,-0.0435473919],[-0.0329419039,0.0499187671,0.0159879029,0.0226416737,0.0572009087,0.0169953778,0.0014623989,-0.059061747],[-0.2327010036,0.1027743742,-0.0122413756,0.0495935939,0.0280871764,-0.0538110025,-0.0385680757,0.123318933],[0.0028765725,-0.003429034,0.046994295,0.0192156862,-0.0064633554,-0.0241745599,-0.0367736556,0.0160626136],[-0.0068317289,0.0291511118,0.0879879147,0.0794397742,0.05882008,-0.0122352736,-0.0035967014,-0.0127760237]],[0.1892332435,-0.0836044699,-0.0343620554,-0.0564836971,-0.0631505176,0.0272518657,0.0095205978,-0.1045202538],[[0.1019221023,-0.0305945147,-0.0359275229,0.1524256468,-0.0245757345,0.149545297],[0.0477291718,-0.0069747292,0.0017543581,-0.0294865649,-0.0035721776,-0.0105535956],[0.0457640737,0.0222507585,-0.0189340338,0.0176823959,0.0232398938,0.1038072333],[-0.0229437519,-0.0037464558,-0.0406899974,0.0335939266,-0.0280832294,-0.035844963],[0.0157823972,-0.0069473935,-0.0812087357,0.0187359527,0.0143456571,0.0173410792],[-0.0285115037,0.0277708899,-0.0401631221,0.012221084,-0.0175570287,0.1729588807],[-0.0449816212,0.0595379509,-0.0334494524,0.1174537018,-0.0340393223,-0.0202259049],[-0.001982389,0.008182928,-0.0544667765,-0.0103572421,-0.0074457065,-0.0234164968]],[0.9318162203,-0.0069479314,-0.1054181308,1.2725764513,1.38625741,-0.0264722481],[[-0.2285993248],[-0.0322982036],[-0.0067237676],[-0.3306575119],[-0.9310822487],[-0.0148510356]],[-1.0206410885]]’}, ‘acc_image_file’: ‘/media/sf_shared/accuracy_job_14_result_10.png’, ‘created’: ‘2018-02-15 17:30:21’, ‘updated’: ‘2018-02-15 17:30:22’, ‘deleted’: ‘’}}

Get the Deep Neural Network Accuracy, JSON and Weights

This will display all the recent training runs in a list sorted by newest.

/opt/antinex/api/tests/get-recent-results.py

Here’s the training node in the list from the run above (yours will look a little different):

{
    "acc_data": {
        "accuracy": 70.9602648927676
    },
    "acc_image_file": "/media/sf_shared/accuracy_job_14_result_10.png",
    "created": "2018-02-15 17:30:21",
    "deleted": "",
    "error_data": null,
    "id": 10,
    "job_id": 14,
    "model_json": "{\"class_name\": \"Sequential\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_1\", \"trainable\": true, \"batch_input_shape\": [null, 68], \"dtype\": \"float32\", \"units\": 8, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"RandomUniform\", \"config\": {\"minval\": -0.05, \"maxval\": 0.05, \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_2\", \"trainable\": true, \"units\": 6, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"RandomUniform\", \"config\": {\"minval\": -0.05, \"maxval\": 0.05, \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_3\", \"trainable\": true, \"units\": 1, \"activation\": \"sigmoid\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"RandomUniform\", \"config\": {\"minval\": -0.05, \"maxval\": 0.05, \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}], \"keras_version\": \"2.1.4\", \"backend\": \"tensorflow\"}",
    "model_weights": {
        "weights": "[[[0.1209325939,0.125004366,0.04392628,0.0058005759,0.0283837207,0.1084360257,-0.0221137945,0.1256226152],[0.0659536794,0.0419033654,0.0170708448,-0.0357947014,0.0545291603,0.0852750689,-0.0538051911,0.0699182898],[-0.1545251906,0.1111956462,-0.0147213368,0.0731624961,0.0864041299,-0.0017828628,-0.0178274736,0.1216073707],[0.1336804777,0.1023893878,0.0170907527,0.0518929921,0.0966836065,0.1597969532,0.0217716675,0.085526742],[0.1376502961,0.0593057014,0.0694345012,-0.0378300808,-0.0057742135,0.0791756362,0.0235338192,0.0824202821],[0.1233218759,0.0594531633,-0.0086939791,-0.0387902856,-0.0360716954,0.0850752816,-0.0114007629,0.0921288431],[-0.1866215467,0.0660640672,-0.0012092546,0.0663910806,0.0470964499,-0.0053454894,-0.0322002359,0.1119380593],[-0.2092835754,0.0618410148,0.0634177029,0.0136457058,0.0184208602,-0.0168401636,-0.0061000162,0.0907672495],[-0.1857144684,0.1216576323,0.0256078299,0.0622631498,0.0746099502,-0.0211249851,-0.058073774,0.0694563985],[-0.1634339541,0.0696025267,0.0837683603,0.092234768,0.0956235006,-0.0282980427,-0.040868368,0.113351725],[-0.1704672724,0.0691201314,0.071548,0.069260262,0.1131449491,-0.0108540468,-0.0425567217,0.0640222654],[-0.2344770879,0.0428572707,0.0793405771,0.0114616835,0.0313309282,0.0125392843,0.0246058684,0.0830053911],[-0.2056925297,0.0455387048,0.0595687404,0.0481434427,0.0567863956,-0.0198212601,0.0394698866,0.1153029948],[-0.1900539547,0.0648706928,0.0755348429,0.0531642176,0.0225196443,-0.037876796,-0.050964918,0.1065156162],[-0.1578101218,0.0851738676,0.080335848,0.1033686772,0.0672588199,-0.0466286503,0.0274061691,0.1454075873],[-0.1649408489,0.1129061654,0.0413498543,0.0406241789,0.0480666906,-0.0626311898,-0.0147714363,0.1095488593],[-0.1902400851,0.0546266772,-0.0002129045,0.081249468,0.0261988528,-0.0456862338,0.033373639,0.1446813047],[-0.2027439624,0.1166701987,0.0509505421,0.0388729684,0.0726077035,-0.0771305785,0.0307749175,0.0881667733],[-0.183378011,0.0356091969,0.0145586552,0.0244816709,0.0284536369,-0.0071689375,-0.0315265507,0.1486145407],[-0.1512038261,0.0764901713,0.061774157,0.0451634862,0.062191762,-0.0525121838,0.0302008335,0.0902092978],[-0.2147508562,0.1103100851,0.017692728,0.0237773284,0.051074788,-0.0431679115,0.0140604237,0.0951612145],[-0.1971089989,0.0693886876,0.0808551386,0.0206196532,0.0207697973,-0.039527256,-0.0105302734,0.0831609368],[-0.2245272845,0.1265042126,0.0620332398,0.0791124329,0.0703641251,-0.0618047714,-0.0481486395,0.1099268496],[-0.1919761449,0.1043844149,0.028273426,0.0244386699,0.0346066169,-0.0108972676,-0.0562972091,0.1470609605],[-0.1688121408,0.082510218,0.0394482091,0.0875071362,0.1120331734,-0.0286579132,-0.0526521802,0.0697672367],[-0.1516744047,0.0463477261,0.0772118047,0.0861110315,0.0297314562,-0.0423166379,-0.006591965,0.0826482922],[-0.2255488038,0.1261228472,0.0011998075,0.0771774799,0.0211141836,-0.0571117215,0.0122114196,0.1291222423],[-0.2126398236,0.1168462709,0.0215776451,0.0146405725,0.1047314554,-0.0019828572,-0.000542541,0.0922133848],[0.2239516675,-0.1272274554,-0.0545392334,-0.0690253899,-0.0240141228,0.0290964283,-0.0048073386,-0.1407266706],[-0.1601528823,0.0873816535,0.0222081486,0.0657906458,0.0444654338,-0.0557228811,0.0258848574,0.1467054784],[0.1103282571,0.062621966,-0.0022228661,0.0238575842,0.0446507819,0.0041218554,-0.009616375,0.0077553303],[-0.190007925,0.0701109022,-0.0119564654,0.0722162873,0.0221073441,-0.0255252719,-0.0031145217,0.1183288619],[-0.2136628032,0.0534639172,0.0714060888,0.0613537244,0.0842673779,-0.0301567074,0.0307384171,0.0729675815],[-0.1955242753,0.0495460741,0.0524940416,0.0604530908,0.0595010929,0.0166093316,0.0391650833,0.0827598944],[-0.0501433276,0.1038881019,0.0417513065,0.0355237499,0.0351028442,-0.0003712648,-0.0190300811,0.0249827243],[-0.1487564296,0.1013090238,0.0214297064,0.0933733582,0.0616822541,-0.0710285828,-0.0045771608,0.1217406169],[-0.1543573439,0.0395659693,0.030846579,0.0086379368,0.0861991048,-0.048596736,-0.0271895695,0.1182741746],[-0.1961124837,0.0571020655,0.0076962849,0.0844913498,0.0193723273,0.0068046544,0.0302888621,0.143293947],[-0.0618059374,0.1257505566,0.0142706381,0.1050991416,0.0157455113,-0.0454091579,-0.0202189703,0.0501390435],[-0.2188960463,0.0440853648,0.07568717,0.0457809009,0.0491918027,-0.0327213667,-0.0431831405,0.1276204884],[0.1394346952,-0.088656649,-0.0315131843,-0.0401459411,-0.0437365025,0.0486980379,0.0174318217,-0.0507184826],[-0.1698808372,0.0357401222,0.0317347683,0.0144515438,0.0194635,0.0180209279,-0.0143435514,0.0795696303],[-0.06071667,-0.0370089039,-0.0037398755,0.0016650554,-0.0761372149,-0.0823373273,-0.0147782043,-0.0841278732],[0.0316180326,-0.0783421397,0.000754284,-0.0383572131,-0.0244521741,-0.0295800623,-0.0005746271,-0.0404013805],[-0.0592732318,-0.0370828509,0.0262605324,0.0704324692,0.006007982,-0.117112644,-0.0359724984,0.011283231],[-0.0184435453,-0.0733112022,-0.0283645988,0.0459322594,-0.0326308981,-0.0342220962,0.0310261045,-0.1323656887],[-0.0456168354,0.0782427266,0.057690192,0.0134688364,0.0762039647,-0.015021774,-0.0588690341,0.1178085133],[-0.2152218074,0.0512532629,-0.0107616447,0.0344684459,0.0744112581,0.0065470482,-0.0018064472,0.1076126173],[-0.1873236597,0.1053858474,-0.011723455,0.058383096,0.1098771915,-0.0099292547,-0.0258859675,0.0824473128],[-0.1549893022,0.0844767764,0.0727496445,0.0866250396,0.0313318856,-0.0712586939,0.011944362,0.1336840689],[-0.2329286635,0.1031851396,0.0821548253,0.0666918531,0.0968727544,-0.0197768845,0.0252549183,0.0636012107],[-0.216961056,0.0476008765,0.041463919,0.0264160633,0.0959576741,-0.0256117992,-0.0261161607,0.0812392458],[-0.1800720841,0.0473946743,0.0632657334,0.0181837026,0.0440427177,-0.0560101122,-0.0301355477,0.1441762149],[-0.0096860044,-0.0820260122,-0.0727077052,-0.0164042395,-0.0778253227,-0.0372423194,-0.2164034247,-0.0453402027],[-0.2012402564,0.0422893129,0.0804941952,0.09306252,0.1128023267,-0.0451305024,-0.0048267297,0.0649067611],[-0.2305155843,0.0032782811,-0.0235605519,-0.0537962541,-0.0118097244,-0.0141736846,0.1753456295,0.0044183824],[-0.2079010457,0.069323428,0.0517042466,0.0940786377,-0.0039390367,-0.0135685029,-0.0057561048,0.0483344011],[-0.179656595,0.0572441481,0.0705757067,0.0079024527,0.0245193392,-0.0251601636,0.0155755458,0.0752719566],[-0.1713959128,0.1208590493,0.0346451364,0.0616195463,0.0427664891,0.0182297006,-0.0133397086,0.1184593365],[-0.2043197453,0.0445172973,0.0081551857,0.092943117,0.0198938642,-0.0723016635,0.0256206822,0.0992775932],[-0.2159956694,0.0412812345,0.0545012057,0.0114885671,0.0877385288,-0.0408159904,0.008009661,0.0881702825],[-0.1577915996,0.0643116087,0.0129699642,0.028255403,0.1068808511,-0.0763266757,0.0346705541,0.0588488467],[-0.2040084004,0.0085537154,-0.0356605425,-0.0487324521,0.0015256398,-0.0052802409,0.1076657921,-0.0199613888],[-0.2131965607,-0.0604146346,-0.0845651999,0.0071733561,-0.0342927612,-0.0888390467,-0.0313468054,-0.0435473919],[-0.0329419039,0.0499187671,0.0159879029,0.0226416737,0.0572009087,0.0169953778,0.0014623989,-0.059061747],[-0.2327010036,0.1027743742,-0.0122413756,0.0495935939,0.0280871764,-0.0538110025,-0.0385680757,0.123318933],[0.0028765725,-0.003429034,0.046994295,0.0192156862,-0.0064633554,-0.0241745599,-0.0367736556,0.0160626136],[-0.0068317289,0.0291511118,0.0879879147,0.0794397742,0.05882008,-0.0122352736,-0.0035967014,-0.0127760237]],[0.1892332435,-0.0836044699,-0.0343620554,-0.0564836971,-0.0631505176,0.0272518657,0.0095205978,-0.1045202538],[[0.1019221023,-0.0305945147,-0.0359275229,0.1524256468,-0.0245757345,0.149545297],[0.0477291718,-0.0069747292,0.0017543581,-0.0294865649,-0.0035721776,-0.0105535956],[0.0457640737,0.0222507585,-0.0189340338,0.0176823959,0.0232398938,0.1038072333],[-0.0229437519,-0.0037464558,-0.0406899974,0.0335939266,-0.0280832294,-0.035844963],[0.0157823972,-0.0069473935,-0.0812087357,0.0187359527,0.0143456571,0.0173410792],[-0.0285115037,0.0277708899,-0.0401631221,0.012221084,-0.0175570287,0.1729588807],[-0.0449816212,0.0595379509,-0.0334494524,0.1174537018,-0.0340393223,-0.0202259049],[-0.001982389,0.008182928,-0.0544667765,-0.0103572421,-0.0074457065,-0.0234164968]],[0.9318162203,-0.0069479314,-0.1054181308,1.2725764513,1.38625741,-0.0264722481],[[-0.2285993248],[-0.0322982036],[-0.0067237676],[-0.3306575119],[-0.9310822487],[-0.0148510356]],[-1.0206410885]]"
    },
    "status": "finished",
    "updated": "2018-02-15 17:30:22",
    "user_id": 1,
    "user_name": "root",
    "version": 1
}