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Added the URI encoded injection code feature #28

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Added the URI encoded injection code feature #28

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044a176
Added the URI encoded injection code feature
Dec 4, 2018
f9db881
Added spaces instead of tabs
DeweshSingh Dec 17, 2018
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73 changes: 32 additions & 41 deletions Generator/ga_main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,8 @@
import pandas as pd
from decimal import Decimal
from util import Utilty
import urllib
import urllib.parse

# Type of printing.
OK = 'ok' # [*]
Expand Down Expand Up @@ -97,11 +99,20 @@ def create_genom(self, df_gene):
# Evaluation.
def evaluation(self, obj_ga, df_gene, eval_place, individual_idx):
# Build html syntax.
indivisual = self.util.transform_gene_num2str(df_gene, obj_ga.genom_list)
html = self.template.render({eval_place: indivisual})
individual = self.util.transform_gene_num2str(df_gene, obj_ga.genom_list)
encoded_individual = urllib.parse.quote(individual)

html = self.template.render({eval_place: individual})
eval_html_path = self.util.join_path(self.html_dir, self.html_file.replace('*', str(individual_idx)))

encoded_html = self.template.render({eval_place: encoded_individual})
encoded_eval_html_path = self.util.join_path(self.html_dir, self.html_file.replace('*', 'Encoded'+str(individual_idx)))

with codecs.open(eval_html_path, 'w', encoding='utf-8') as fout:
fout.write(html)

with codecs.open(encoded_eval_html_path, 'w', encoding='utf-8') as fout:
fout.write(encoded_html)

# Evaluate html syntax using tidy.
command = self.html_checker + ' ' + self.html_checker_option + ' ' + \
Expand Down Expand Up @@ -131,25 +142,23 @@ def evaluation(self, obj_ga, df_gene, eval_place, individual_idx):

# Evaluate running script using selenium.
selenium_score, error_flag = self.util.check_individual_selenium(self.obj_browser, eval_html_path)
if error_flag:

# Evaluate the running encoded script using selenium
encoded_selenium_score, encoded_error_flag = self.util.check_individual_selenium(self.obj_browser, eval_html_path)

if error_flag and encoded_error_flag:
return None, 1

# Check result of selenium.
if selenium_score > 0:
self.util.print_message(OK, 'Detect running script: "{}" in {}.'.format(indivisual, eval_place))
if selenium_score > 0 or encoded_selenium_score > 0:
self.util.print_message(OK, 'Detect running script: "{}" in {}.'.format(individual, eval_place))

# compute score for running script.
int_score += self.bingo_score
self.result_list.append([eval_place, obj_ga.genom_list, indivisual])
self.result_list.append([eval_place, obj_ga.genom_list, individual, encoded_individual])

# Output evaluation results.
self.util.print_message(OK, 'Evaluation result : Browser={} {}, '
'Individual="{} ({})", '
'Score={}'.format(self.obj_browser.name,
self.obj_browser.capabilities['version'],
indivisual,
obj_ga.genom_list,
str(int_score)))
self.util.print_message(OK, 'Evaluation result : Browser={} {}, ''Individual="{} ({})", ''Score={}'.format(self.obj_browser.name,self.obj_browser.capabilities['version'],individual,obj_ga.genom_list,str(int_score)))
return int_score, 0

# Select elite individual.
Expand Down Expand Up @@ -219,12 +228,9 @@ def main(self):
# Create saving file (only header).
save_path = self.util.join_path(self.result_dir, self.result_file.replace('*', self.obj_browser.name))
if os.path.exists(save_path) is False:
pd.DataFrame([], columns=['eval_place', 'sig_vector', 'sig_string']).to_csv(save_path,
mode='w',
header=True,
index=False)
pd.DataFrame([], columns=['eval_place', 'sig_vector', 'sig_string', 'encoded_string']).to_csv(save_path,mode='w',header=True,index=False)

# Evaluate indivisual each evaluating place in html.
# Evaluate individual each evaluating place in html.
for eval_place in self.html_eval_place_list:
self.util.print_message(NOTE, 'Evaluating html place : {}'.format(eval_place))

Expand All @@ -238,22 +244,16 @@ def main(self):
for int_count in range(1, self.max_generation + 1):
self.util.print_message(NOTE, 'Evaluate individual : {}/{} generation.'.format(str(int_count),
self.max_generation))
for indivisual, idx in enumerate(range(self.max_genom_list)):
for individual, idx in enumerate(range(self.max_genom_list)):
self.util.print_message(OK, 'Evaluation individual in {}: '
'{}/{} in {} generation'.format(eval_place,
indivisual + 1,
self.max_genom_list,
str(int_count)))
evaluation_result, eval_status = self.evaluation(current_generation[indivisual],
df_genes,
eval_place,
idx)
'{}/{} in {} generation'.format(eval_place,individual + 1,self.max_genom_list,str(int_count)))
evaluation_result, eval_status = self.evaluation(current_generation[individual],df_genes,eval_place,idx)

idx += 1
if eval_status == 1:
indivisual -= 1
individual -= 1
continue
current_generation[indivisual].setEvaluation(evaluation_result)
current_generation[individual].setEvaluation(evaluation_result)
time.sleep(self.wait_time)

# Select elite's individual.
Expand All @@ -265,27 +265,18 @@ def main(self):
progeny_gene.extend(self.crossover(elite_genes[i - 1], elite_genes[i]))

# Select elite group.
next_generation_individual_group = self.next_generation_gene_create(current_generation,
elite_genes,
progeny_gene)
next_generation_individual_group = self.next_generation_gene_create(current_generation,elite_genes,progeny_gene)

# Mutation
next_generation_individual_group = self.mutation(next_generation_individual_group,
self.individual_mutation_rate,
self.genom_mutation_rate,
df_genes)
next_generation_individual_group = self.mutation(next_generation_individual_group,self.individual_mutation_rate,self.genom_mutation_rate,df_genes)

# Finish evolution computing for current generation.
# Arrange fitness each individual.
fits = [_.getEvaluation() for _ in current_generation]

# evaluate evolution result.
flt_avg = sum(fits) / float(len(fits))
self.util.print_message(NOTE, '{} generation result: '
'Min={}, Max={}, Avg={}.'.format(int_count,
min(fits),
max(fits),
flt_avg))
self.util.print_message(NOTE, '{} generation result: ''Min={}, Max={}, Avg={}.'.format(int_count,min(fits),max(fits),flt_avg))

# Judge fitness.
if flt_avg > self.max_fitness:
Expand Down
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