Code for the paper: https://medium.com/@tarcisioma/a-deep-learning-approach-to-web-application-firewall-c6190a9bd31a
# !pip3 install -q tensorflow_gpu=
# !pip install --upgrade tensorflow
!pip install tensorflow==2.1import tensorflow as tf
print(tf.__version__)2.2.0-rc3
!pip3 install gast==0.3.2!pip3 install -q ktrain�[K |████████████████████████████████| 25.2MB 129kB/s
�[K |████████████████████████████████| 421.8MB 16kB/s
�[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))': /simple/scikit-learn/�[0m
�[K |████████████████████████████████| 6.7MB 59.4MB/s
�[K |████████████████████████████████| 983kB 40.0MB/s
�[K |████████████████████████████████| 245kB 45.8MB/s
�[K |████████████████████████████████| 675kB 44.3MB/s
�[K |████████████████████████████████| 471kB 42.2MB/s
�[K |████████████████████████████████| 450kB 45.9MB/s
�[K |████████████████████████████████| 3.9MB 46.8MB/s
�[K |████████████████████████████████| 890kB 37.0MB/s
�[K |████████████████████████████████| 3.8MB 43.4MB/s
�[K |████████████████████████████████| 1.1MB 47.1MB/s
�[?25h Building wheel for ktrain (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-bert (setup.py) ... �[?25l�[?25hdone
Building wheel for langdetect (setup.py) ... �[?25l�[?25hdone
Building wheel for seqeval (setup.py) ... �[?25l�[?25hdone
Building wheel for syntok (setup.py) ... �[?25l�[?25hdone
Building wheel for gast (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-transformer (setup.py) ... �[?25l�[?25hdone
Building wheel for sacremoses (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-pos-embd (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-multi-head (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-layer-normalization (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-position-wise-feed-forward (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-embed-sim (setup.py) ... �[?25l�[?25hdone
Building wheel for keras-self-attention (setup.py) ... �[?25l�[?25hdone
�[31mERROR: tensorflow-probability 0.10.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.�[0m
import ktrain
print(ktrain.__version__)0.16.2
from google.colab import drive
drive.mount('/content/gdrive')Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly
Enter your authorization code:
··········
Mounted at /content/gdrive
import sys
import os
import randomLABELS = {}
INV_LABELS = {}
for i, label in enumerate(os.listdir('gdrive/My Drive/datasets-payload-identifier/')):
LABELS[i] = label
INV_LABELS[label] = ifrom sklearn.model_selection import train_test_split
dataset_size = {}
target_names = []
def get_dataset(path, test_size=0.25, random_state=42):
X = []
Y = []
for label in os.listdir(path):
target_names.append(label)
with open(os.path.join('gdrive/My Drive/datasets-payload-identifier/', label, 'ALL_FILES.txt')) as f:
lines = f.readlines()
dataset_size[label] = len(lines)
for payload in lines:
if("\n" in payload):
X.append(payload.replace("\n", ""))
Y.append(INV_LABELS[label])
return train_test_split(X, Y, test_size=test_size, random_state=random_state)
x_train, x_test, y_train, y_test = get_dataset('gdrive/My Drive/datasets-payload-identifier/')count = 1
for i in y_test:
if(LABELS[i] == ''):
count+=1
print(count)2010
import matplotlib.pyplot as plt
%matplotlib inline
ax = plt.figure().add_axes([0,0,1,1])
print("Dataset size: ")
for k, v in dataset_size.items():
print("Payload: {} - Size: {}".format(k, v))
payloads = dataset_size.keys()
size = dataset_size.values()
ax.bar(payloads, size)
plt.title("Dataset")
plt.xlabel("Payloads")
plt.ylabel("Size")
plt.show()Dataset size:
Payload: xxe - Size: 115
Payload: path-traversal - Size: 7090
Payload: xss - Size: 9882
Payload: file-inclusion - Size: 7935
Payload: credentials - Size: 6702
Payload: sql - Size: 3550
Payload: email - Size: 7996
Payload: rce - Size: 1036
import ktrain
from ktrain import text
# distillbert - roberta - xlm - albert - bert
#MODELS = ['bert-base-uncased', 'distilbert-base-uncased', 'roberta-large', 'xlm-roberta-base', 'albert-base-v2']
MODEL_NAME = 'distilbert-base-uncased'
#for MODEL_NAME in MODELS:
print("Running model: ".upper(), MODEL_NAME.upper())
print("\n\n\n")
t = text.Transformer(MODEL_NAME, maxlen=500, classes=target_names)
trn = t.preprocess_train(x_train, y_train)
val = t.preprocess_test(x_test, y_test)
model = t.get_classifier()
learner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=6)
learner.fit_onecycle(5e-5, 1)
print("Validating: ")
learner.validate(class_names=t.get_classes())
print("Top losses: ")
learner.view_top_losses(preproc=t)
#print("Confusion Matrix: ")
#predictor = ktrain.get_predictor(learner.model, preproc=t)
#predictor.analyze_valid((x_test, y_test))
print("Finished running model: ", MODEL_NAME)RUNNING MODEL: DISTILBERT-BASE-UNCASED
/usr/local/lib/python3.6/dist-packages/ktrain/text/preprocessor.py:367: UserWarning: The class_names argument is replacing the classes argument. Please update your code.
warnings.warn('The class_names argument is replacing the classes argument. Please update your code.')
preprocessing train...
language: en
train sequence lengths:
mean : 2
95percentile : 5
99percentile : 10
preprocessing test...
language: en
test sequence lengths:
mean : 2
95percentile : 5
99percentile : 9
begin training using onecycle policy with max lr of 5e-05...
Train for 5538 steps, validate for 347 steps
5538/5538 [==============================] - 1425s 257ms/step - loss: 0.1665 - accuracy: 0.9596 - val_loss: 0.0491 - val_accuracy: 0.9855
Validating:
precision recall f1-score support
xxe 0.95 0.71 0.82 28
path-traversal 0.99 0.96 0.98 1831
xss 1.00 1.00 1.00 2467
file-inclusion 0.96 0.99 0.98 1947
credentials 0.99 0.99 0.99 1656
sql 0.98 0.98 0.98 880
email 1.00 1.00 1.00 2009
rce 0.94 0.93 0.93 257
accuracy 0.99 11075
macro avg 0.98 0.95 0.96 11075
weighted avg 0.99 0.99 0.99 11075
Top losses:
----------
id:6149 | loss:9.09 | true:xxe | pred:sql)
----------
id:2742 | loss:8.17 | true:credentials | pred:sql)
----------
id:7819 | loss:8.04 | true:credentials | pred:email)
----------
id:1705 | loss:7.74 | true:sql | pred:credentials)
Finished running model: distilbert-base-uncased
predictor = ktrain.get_predictor(learner.model, preproc=t)predictor.predict(";wget 192.168.1.1 -c -a;ping 12313;")'rce'
predictor.predict_proba(";wget 192.168.1.1 -c -a;ping 12313;")array([9.49885987e-04, 3.35088174e-04, 2.64028879e-03, 1.36631366e-03,
3.13900970e-03, 7.95728900e-03, 1.02633625e-04, 9.83509600e-01],
dtype=float32)
!pip3 install -q git+https://github.com/amaiya/eli5@tfkeras_0_10_1 Building wheel for eli5 (setup.py) ... �[?25l�[?25hdone
predictor.explain(testes[0])<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=sql
(probability <b>1.000</b>, score <b>9.986</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+4.856
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
or
</td>
<tr style="background-color: hsl(120, 100.00%, 87.86%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+2.380
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
1¼1
</td>
<tr style="background-color: hsl(120, 100.00%, 88.47%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+2.210
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
2¼2
</td>
<tr style="background-color: hsl(120, 100.00%, 95.69%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.541
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
predictor.explain(testes[5])<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=xss
(probability <b>1.000</b>, score <b>11.000</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+4.419
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
script
</td>
<tr style="background-color: hsl(120, 100.00%, 89.72%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.707
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
0
</td>
<tr style="background-color: hsl(120, 100.00%, 89.88%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.670
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
alert
</td>
<tr style="background-color: hsl(120, 100.00%, 90.89%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.438
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
type¼vbscript
</td>
<tr style="background-color: hsl(120, 100.00%, 91.09%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.393
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
msgbox
</td>
<tr style="background-color: hsl(120, 100.00%, 95.16%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.582
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
<tr style="background-color: hsl(0, 100.00%, 99.02%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
-0.060
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
type¼vbscript msgbox
</td>
<tr style="background-color: hsl(0, 100.00%, 98.13%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
-0.150
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
script script
</td>
</tbody>
</table>
predictor.explain(testes[10])<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=xss
(probability <b>1.000</b>, score <b>12.494</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+2.641
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
alert
</td>
<tr style="background-color: hsl(120, 100.00%, 81.32%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+2.395
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
script
</td>
<tr style="background-color: hsl(120, 100.00%, 82.97%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+2.099
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
x
</td>
<tr style="background-color: hsl(120, 100.00%, 84.29%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.870
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
0
</td>
<tr style="background-color: hsl(120, 100.00%, 89.61%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.036
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
img
</td>
<tr style="background-color: hsl(120, 100.00%, 89.71%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.021
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
src¼
</td>
<tr style="background-color: hsl(120, 100.00%, 90.33%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.934
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
onerror¼
</td>
<tr style="background-color: hsl(120, 100.00%, 93.28%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.556
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
<tr style="background-color: hsl(0, 100.00%, 98.61%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
-0.059
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
src¼ x
</td>
</tbody>
</table>
predictor.explain(testes[8])<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=sql
(probability <b>1.000</b>, score <b>12.881</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+3.653
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
select
</td>
<tr style="background-color: hsl(120, 100.00%, 80.13%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+3.620
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
union
</td>
<tr style="background-color: hsl(120, 100.00%, 80.82%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+3.442
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
1
</td>
<tr style="background-color: hsl(120, 100.00%, 88.38%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+1.682
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
all
</td>
<tr style="background-color: hsl(120, 100.00%, 95.13%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.485
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
predictor.save('distilbert-base-uncased-FINAL')reloaded_predictor = ktrain.load_predictor('gdrive/My Drive/BERT-TCC/distilbert-base-uncased-FINAL')testes = ["' or 1¼1-- ' or 2¼2--", "' or 1¼1-- '¼'", '";alert(0);" "*alert(0)*"', "',alert(0),b' '%0aalert(0)%0a'",
'alert(0) %00alert(0)', '<script>alert(0)</script> <script type¼vbscript>MsgBox(0)', '</script>', '\' OR ""¼\' \'/**/OR/**/""¼\'',
"' union select 1;-- ' union all select 1;--", '<script>alert(0)</script> <SCRIPT>alert(0)</SCRIPT>',
'<script>alert(0)</script> <img src¼"x:x" onerror¼"alert(0)">', '</img>', '<img src¼"x:x"', 'onerror¼"alert(0)">', '</img>',
'<img src¼"x:x" onerror¼"alert(0)">', '</img>', "<img src¼'x:x'","onerror¼'alert(0)'></img>", '<img src¼x:x onerror¼alert(0)//>',
'</img>', '<img src¼x:x onerror¼alert', '(0)//></img>', '<img src¼http://0x.lv/ onload¼', 'alert(0)//></img>', '<img src¼http://0x.lv/',
'onload¼alert(0)//></img>', '<marquee onstart¼alert(0)//>', '1 or 1¼1 (1)or(1)¼(1)', 'alert(0) deletetypeoftypeoftypeoftypeof', 'typeof',
'typeofalert(0)', 'eval(name) x¼this.name', 'x(0?$:name+1)', "2a''-1 ^''0 2a''-1 ^ ' 0''' and (select mid(user,", "1 /1,1/ 1)from'mysql'.user limit 1)",
"rlike ''r", 'xyz¼this', 'zyx¼xyz[1].alert', 'zyx(1)', 'xyz¼Iterator([this]).next()', 'zyx¼xyz[1].alert', 'zyx(1)']
for exploit in testes:
print(exploit, " >>> ", reloaded_predictor.predict(exploit))' or 1¼1-- ' or 2¼2-- >>> sql
' or 1¼1-- '¼' >>> sql
";alert(0);" "*alert(0)*" >>> xss
',alert(0),b' '%0aalert(0)%0a' >>> sql
alert(0) %00alert(0) >>> xss
<script>alert(0)</script> <script type¼vbscript>MsgBox(0) >>> xss
</script> >>> xss
' OR ""¼' '/**/OR/**/""¼' >>> sql
' union select 1;-- ' union all select 1;-- >>> sql
<script>alert(0)</script> <SCRIPT>alert(0)</SCRIPT> >>> xss
<script>alert(0)</script> <img src¼"x:x" onerror¼"alert(0)"> >>> xss
</img> >>> xss
<img src¼"x:x" >>> xss
onerror¼"alert(0)"> >>> xss
</img> >>> xss
<img src¼"x:x" onerror¼"alert(0)"> >>> xss
</img> >>> xss
<img src¼'x:x' >>> xss
onerror¼'alert(0)'></img> >>> xss
<img src¼x:x onerror¼alert(0)//> >>> xss
</img> >>> xss
<img src¼x:x onerror¼alert >>> xss
(0)//></img> >>> sql
<img src¼http://0x.lv/ onload¼ >>> xss
alert(0)//></img> >>> xss
<img src¼http://0x.lv/ >>> xss
onload¼alert(0)//></img> >>> xss
<marquee onstart¼alert(0)//> >>> xss
1 or 1¼1 (1)or(1)¼(1) >>> sql
alert(0) deletetypeoftypeoftypeoftypeof >>> xss
typeof >>> credentials
typeofalert(0) >>> xss
eval(name) x¼this.name >>> rce
x(0?$:name+1) >>> sql
2a''-1 ^''0 2a''-1 ^ ' 0''' and (select mid(user, >>> sql
1 /1,1/ 1)from'mysql'.user limit 1) >>> sql
rlike ''r >>> sql
xyz¼this >>> credentials
zyx¼xyz[1].alert >>> xss
zyx(1) >>> xss
xyz¼Iterator([this]).next() >>> xss
zyx¼xyz[1].alert >>> xss
zyx(1) >>> xss
# https://portswigger.net/web-security/cross-site-scripting/cheat-sheet
with open("gdrive/My Drive/BERT-TCC/xss_tests.txt", 'r') as f:
xss_payloads = f.readlines()
xss_accuracy = 0
for payload in xss_payloads:
if(reloaded_predictor.predict(payload)== 'xss'):
xss_accuracy += 1
print("XSS lenght: {}".format(len(xss_payloads)))
print("XSS identified: {}".format(xss_accuracy))XSS lenght: 8492
XSS identified: 8492
# https://portswigger.net/web-security/sql-injection/cheat-sheet
with open("gdrive/My Drive/BERT-TCC/sql_tests.txt", 'r') as f:
sql_payloads = f.readlines()
sql_accuracy = 0
for payload in sql_payloads:
if(reloaded_predictor.predict(payload) == 'sql'):
sql_accuracy += 1
print("sql lenght: {}".format(len(sql_payloads)))
print("sql identified: {}".format(sql_accuracy))
print('accuracy:', sql_accuracy*100/len(sql_payloads))sql lenght: 51
sql identified: 45
accuracy: 88.23529411764706
reloaded_predictor.explain("admin' or '1'='1'--")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=sql
(probability <b>1.000</b>, score <b>10.448</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+9.926
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 97.45%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.523
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="background-color: hsl(120, 100.00%, 63.02%); opacity: 0.98" title="2.372">admin</span><span style="opacity: 0.80">' </span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="2.653">or</span><span style="opacity: 0.80"> '</span><span style="background-color: hsl(120, 100.00%, 61.55%); opacity: 0.99" title="2.507">1</span><span style="opacity: 0.80">'='</span><span style="background-color: hsl(120, 100.00%, 61.95%); opacity: 0.99" title="2.470">1</span><span style="opacity: 0.80">'--</span>
</p>
reloaded_predictor.explain("' union (select NULL, (select @@version)) --")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=sql
(probability <b>1.000</b>, score <b>10.872</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+10.313
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 97.40%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.559
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="opacity: 0.80">' </span><span style="background-color: hsl(120, 100.00%, 62.22%); opacity: 0.98" title="1.952">union</span><span style="opacity: 0.80"> (</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="2.118">select</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 61.08%); opacity: 0.99" title="2.037">null</span><span style="opacity: 0.80">, (</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="2.118">select</span><span style="opacity: 0.80"> @@</span><span style="background-color: hsl(120, 100.00%, 60.43%); opacity: 1.00" title="2.086">version</span><span style="opacity: 0.80">)) --</span>
</p>
reloaded_predictor.explain("AND (SELECT * FROM (SELECT(SLEEP(5)))bAKL) AND 'vRxe'='vRxe")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=sql
(probability <b>1.000</b>, score <b>13.774</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+13.250
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 97.92%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.524
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="background-color: hsl(120, 100.00%, 60.72%); opacity: 0.99" title="1.412">and</span><span style="opacity: 0.80"> (</span><span style="background-color: hsl(120, 100.00%, 60.55%); opacity: 1.00" title="1.421">select</span><span style="opacity: 0.80"> * </span><span style="background-color: hsl(120, 100.00%, 67.39%); opacity: 0.95" title="1.083">from</span><span style="opacity: 0.80"> (</span><span style="background-color: hsl(120, 100.00%, 60.55%); opacity: 1.00" title="1.421">select</span><span style="opacity: 0.80">(</span><span style="background-color: hsl(120, 100.00%, 66.97%); opacity: 0.95" title="1.102">sleep</span><span style="opacity: 0.80">(</span><span style="background-color: hsl(120, 100.00%, 64.06%); opacity: 0.97" title="1.244">5</span><span style="opacity: 0.80">)))</span><span style="background-color: hsl(120, 100.00%, 62.90%); opacity: 0.98" title="1.301">bakl</span><span style="opacity: 0.80">) </span><span style="background-color: hsl(120, 100.00%, 60.72%); opacity: 0.99" title="1.412">and</span><span style="opacity: 0.80"> '</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="1.449">vrxe</span><span style="opacity: 0.80">'='</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="1.449">vrxe</span>
</p>
reloaded_predictor.explain("<form action=javascript:alert(1)><input type=submit>")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=xss
(probability <b>1.000</b>, score <b>8.654</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+8.014
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 96.59%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.640
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="opacity: 0.80"><</span><span style="background-color: hsl(120, 100.00%, 82.36%); opacity: 0.86" title="0.678">form</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 92.08%); opacity: 0.82" title="0.216">action</span><span style="opacity: 0.80">=</span><span style="background-color: hsl(120, 100.00%, 60.18%); opacity: 1.00" title="2.171">javascript</span><span style="opacity: 0.80">:</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="2.185">alert</span><span style="opacity: 0.80">(</span><span style="background-color: hsl(0, 100.00%, 75.86%); opacity: 0.90" title="-1.062">1</span><span style="opacity: 0.80">)><</span><span style="background-color: hsl(0, 100.00%, 78.60%); opacity: 0.88" title="-0.894">input</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 64.25%); opacity: 0.97" title="1.861">type</span><span style="opacity: 0.80">=</span><span style="background-color: hsl(120, 100.00%, 69.06%); opacity: 0.94" title="1.514">submit</span><span style="opacity: 0.80">></span>
</p>
reloaded_predictor.explain("<svg id=alert(1) onload=eval(id)>")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=xss
(probability <b>1.000</b>, score <b>11.535</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+11.224
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 98.37%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.312
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="opacity: 0.80"><</span><span style="background-color: hsl(120, 100.00%, 65.79%); opacity: 0.96" title="2.278">svg</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 68.73%); opacity: 0.94" title="2.004">id</span><span style="opacity: 0.80">=</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="2.848">alert</span><span style="opacity: 0.80">(</span><span style="background-color: hsl(0, 100.00%, 91.47%); opacity: 0.82" title="-0.313">1</span><span style="opacity: 0.80">) </span><span style="background-color: hsl(120, 100.00%, 69.63%); opacity: 0.93" title="1.922">onload</span><span style="opacity: 0.80">=</span><span style="background-color: hsl(120, 100.00%, 73.75%); opacity: 0.91" title="1.561">eval</span><span style="opacity: 0.80">(</span><span style="background-color: hsl(120, 100.00%, 68.73%); opacity: 0.94" title="2.004">id</span><span style="opacity: 0.80">)></span>
</p>
reloaded_predictor.explain("..%c0%qf..%c0%qf..%c0%qfetc%c0%qfissue")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=path-traversal
(probability <b>1.000</b>, score <b>9.183</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+9.280
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(0, 100.00%, 99.18%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
-0.096
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="opacity: 0.80">..%</span><span style="background-color: hsl(120, 100.00%, 94.30%); opacity: 0.81" title="0.271">c0</span><span style="opacity: 0.80">%</span><span style="background-color: hsl(120, 100.00%, 94.91%); opacity: 0.81" title="0.230">qf</span><span style="opacity: 0.80">..%</span><span style="background-color: hsl(120, 100.00%, 94.30%); opacity: 0.81" title="0.271">c0</span><span style="opacity: 0.80">%</span><span style="background-color: hsl(120, 100.00%, 94.91%); opacity: 0.81" title="0.230">qf</span><span style="opacity: 0.80">..%</span><span style="background-color: hsl(120, 100.00%, 87.55%); opacity: 0.84" title="0.826">c0</span><span style="opacity: 0.80">%</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="4.377">qfetc</span><span style="opacity: 0.80">%</span><span style="background-color: hsl(120, 100.00%, 79.22%); opacity: 0.88" title="1.718">c0</span><span style="opacity: 0.80">%</span><span style="background-color: hsl(120, 100.00%, 61.32%); opacity: 0.99" title="4.172">qfissue</span>
</p>
reloaded_predictor.explain("nc -lvvp 4446 -e /bin/sh|")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=rce
(probability <b>0.999</b>, score <b>6.210</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+6.316
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(0, 100.00%, 98.85%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
-0.106
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="background-color: hsl(120, 100.00%, 68.83%); opacity: 0.94" title="1.320">nc</span><span style="opacity: 0.80"> -</span><span style="background-color: hsl(120, 100.00%, 62.02%); opacity: 0.99" title="1.751">lvvp</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 95.90%); opacity: 0.81" title="0.073">4446</span><span style="opacity: 0.80"> -</span><span style="background-color: hsl(120, 100.00%, 75.70%); opacity: 0.90" title="0.925">e</span><span style="opacity: 0.80"> /</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="1.885">bin</span><span style="opacity: 0.80">/</span><span style="background-color: hsl(120, 100.00%, 85.39%); opacity: 0.85" title="0.447">sh</span><span style="opacity: 0.80">|</span>
</p>
reloaded_predictor.explain("%2 -n 21 127.0.0.1||`ping -c 21 127.0.0.1` #' |ping -n 21 127.0.0.1||`ping -c 21 127.0.0.1` #\" |ping -n 21 127.0.0.1")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=rce
(probability <b>0.986</b>, score <b>4.183</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+4.009
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 97.77%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.174
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="opacity: 0.80">%</span><span style="background-color: hsl(0, 100.00%, 62.54%); opacity: 0.98" title="-0.313">2</span><span style="opacity: 0.80"> -</span><span style="background-color: hsl(0, 100.00%, 88.52%); opacity: 0.83" title="-0.058">n</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(0, 100.00%, 90.08%); opacity: 0.83" title="-0.047">21</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 82.47%); opacity: 0.86" title="0.106">127</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(0, 100.00%, 98.76%); opacity: 0.80" title="-0.002">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 82.56%); opacity: 0.86" title="0.105">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 94.39%); opacity: 0.81" title="0.021">1</span><span style="opacity: 0.80">||`</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="0.344">ping</span><span style="opacity: 0.80"> -</span><span style="background-color: hsl(120, 100.00%, 78.10%); opacity: 0.88" title="0.146">c</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 98.20%); opacity: 0.80" title="0.004">21</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 82.47%); opacity: 0.86" title="0.106">127</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(0, 100.00%, 98.76%); opacity: 0.80" title="-0.002">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 82.56%); opacity: 0.86" title="0.105">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 94.39%); opacity: 0.81" title="0.021">1</span><span style="opacity: 0.80">` #' |</span><span style="background-color: hsl(120, 100.00%, 67.42%); opacity: 0.95" title="0.257">ping</span><span style="opacity: 0.80"> -</span><span style="background-color: hsl(0, 100.00%, 88.47%); opacity: 0.83" title="-0.058">n</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(0, 100.00%, 90.08%); opacity: 0.83" title="-0.047">21</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 82.47%); opacity: 0.86" title="0.106">127</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(0, 100.00%, 98.76%); opacity: 0.80" title="-0.002">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 82.56%); opacity: 0.86" title="0.105">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 94.39%); opacity: 0.81" title="0.021">1</span><span style="opacity: 0.80">||`</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="0.344">ping</span><span style="opacity: 0.80"> -</span><span style="background-color: hsl(120, 100.00%, 78.10%); opacity: 0.88" title="0.146">c</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 98.20%); opacity: 0.80" title="0.004">21</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 82.47%); opacity: 0.86" title="0.106">127</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(0, 100.00%, 98.76%); opacity: 0.80" title="-0.002">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 82.56%); opacity: 0.86" title="0.105">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 94.39%); opacity: 0.81" title="0.021">1</span><span style="opacity: 0.80">` #" |</span><span style="background-color: hsl(120, 100.00%, 67.42%); opacity: 0.95" title="0.257">ping</span><span style="opacity: 0.80"> -</span><span style="background-color: hsl(0, 100.00%, 88.47%); opacity: 0.83" title="-0.058">n</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(0, 100.00%, 90.08%); opacity: 0.83" title="-0.047">21</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(120, 100.00%, 82.47%); opacity: 0.86" title="0.106">127</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(0, 100.00%, 98.76%); opacity: 0.80" title="-0.002">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 82.56%); opacity: 0.86" title="0.105">0</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 78.43%); opacity: 0.88" title="0.142">1</span>
</p>
import numpy as np
reloaded_predictor.analyze_valid((x_test, y_test))reloaded_predictor.explain("/var/apache2/logs/access_log")<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=file-inclusion
(probability <b>1.000</b>, score <b>9.201</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+8.648
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 97.08%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.553
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="opacity: 0.80">/</span><span style="background-color: hsl(120, 100.00%, 60.27%); opacity: 1.00" title="2.179">var</span><span style="opacity: 0.80">/</span><span style="background-color: hsl(120, 100.00%, 60.10%); opacity: 1.00" title="2.193">apache2</span><span style="opacity: 0.80">/</span><span style="background-color: hsl(120, 100.00%, 61.60%); opacity: 0.99" title="2.076">logs</span><span style="opacity: 0.80">/</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="2.200">access_log</span>
</p>
reloaded_predictor.explain('<!DOCTYPE foo [<!ENTITY xxe7eb97 SYSTEM "file:///etc/passwd"> ]>')<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=xxe
(probability <b>0.834</b>, score <b>1.345</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.754
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 83.14%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.591
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="opacity: 0.80"><!</span><span style="background-color: hsl(120, 100.00%, 82.63%); opacity: 0.86" title="0.045">doctype</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(0, 100.00%, 67.56%); opacity: 0.95" title="-0.110">foo</span><span style="opacity: 0.80"> [<!</span><span style="background-color: hsl(0, 100.00%, 74.49%); opacity: 0.91" title="-0.078">entity</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(0, 100.00%, 86.46%); opacity: 0.84" title="-0.032">xxe7eb97</span><span style="opacity: 0.80"> </span><span style="background-color: hsl(0, 100.00%, 87.73%); opacity: 0.84" title="-0.027">system</span><span style="opacity: 0.80"> "</span><span style="background-color: hsl(0, 100.00%, 89.37%); opacity: 0.83" title="-0.022">file</span><span style="opacity: 0.80">:///</span><span style="background-color: hsl(0, 100.00%, 60.00%); opacity: 1.00" title="-0.148">etc</span><span style="opacity: 0.80">/</span><span style="background-color: hsl(0, 100.00%, 85.21%); opacity: 0.85" title="-0.036">passwd</span><span style="opacity: 0.80">"> ]></span>
</p>
reloaded_predictor.explain('ZAQ!2wsx')<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=credentials
(probability <b>0.977</b>, score <b>3.198</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+3.003
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(120, 100.00%, 97.06%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+0.195
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="3.678">zaq</span><span style="opacity: 0.80">!</span><span style="background-color: hsl(0, 100.00%, 87.80%); opacity: 0.84" title="-0.674">2wsx</span>
</p>
reloaded_predictor.explain('[email protected]')<style>
table.eli5-weights tr:hover {
filter: brightness(85%);
}
<p style="margin-bottom: 0.5em; margin-top: 0em">
<b>
y=email
(probability <b>0.994</b>, score <b>5.527</b>)
top features
<table class="eli5-weights"
style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;">
<thead>
<tr style="border: none;">
<th style="padding: 0 1em 0 0.5em; text-align: right; border: none;" title="Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if "show_feature_values" is True.">
Contribution<sup>?</sup>
</th>
<th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th>
</tr>
</thead>
<tbody>
<tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
+5.827
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
Highlighted in text (sum)
</td>
<tr style="background-color: hsl(0, 100.00%, 97.49%); border: none;">
<td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">
-0.300
</td>
<td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">
<BIAS>
</td>
</tbody>
</table>
<p style="margin-bottom: 2.5em; margin-top:-0.5em;">
<span style="background-color: hsl(120, 100.00%, 95.75%); opacity: 0.81" title="0.231">tarcisio_marinho09</span><span style="opacity: 0.80">@</span><span style="background-color: hsl(120, 100.00%, 78.56%); opacity: 0.88" title="2.328">hotmail</span><span style="opacity: 0.80">.</span><span style="background-color: hsl(120, 100.00%, 60.00%); opacity: 1.00" title="5.674">com</span>
</p>