def email_detail_template(self, email_id=''):
title='SHIVA honeypot: view email'
emails = backend_operations.retrieve_by_ids([email_id])
# display error message and terminate
if not emails:
template = Template('<%include file="view_email.html"/>', lookup=self.template_lookup, output_encoding='utf-8', encoding_errors='replace')
return template.render(title=title)
mailFields = emails[0]
if mailFields:
# store html content to static file if it doesn't exist
staticHtmlFile = self.rawHtmlPath + '/' + email_id
if not os.path.exists(staticHtmlFile):
f = open(staticHtmlFile, 'w')
if f:
f.write(mailFields['html'].encode('utf8'))
f.close()
else:
staticHtmlFile = ''
email_result = backend_operations.get_results_of_email(mailFields['s_id'])
template = Template('<%include file="view_email.html"/>', lookup=self.template_lookup, output_encoding='utf-8', encoding_errors='replace')
return template.render(title=title, email_result=email_result, mailFields=mailFields, attachmentsPath=self.attachmentsPath,staticHtmlFile=staticHtmlFile)
def prepare_matrix():
"""
reads results of learning into database and returns them as a matrix
suitable for further processing
Method should be called when database is in consistent state.
Produced matrix has format [M + 1, N + 1]
M is count of emails in database, first two rows contains rule codes.
Firs column of each of M rows contains derived status (1 for phishing, 0 for spam)
Entries [0][0], [1][0] are constant with no practical meaning
Matrix format:
[ '_code' , code1 , code2 ... codeN ]
[ derived_status1 , rule_1_1_result, rule_1_2_result ... rule_1_N_result ]
[ derived_status2 , rule_2_1_result, rule_2_2_result ... rule_2_N_result ]
. . . .
. . . .
. . . .
[ derived_statusM , rule_M_1_result, rule_M_2_result ... rule_M_N_result ]
"""
matrix = []
# indicator of walkthrough
first_loop = True
for email_id in backend_operations.get_email_ids():
email_result = backend_operations.get_results_of_email(email_id)
if "rules" in email_result:
# sort rules to ensure same order in all rows of matrix
sorted_rules = sorted(email_result["rules"], key=lambda a: a["code"])
# add first row into matrix (codes) during first walkthrough
if first_loop:
first_row = ["_derived_result"]
first_row.extend(map(lambda a: a["code"], sorted_rules))
matrix.append(first_row)
first_loop = False
# append row of matrix
sorted_resuls_vector = [1] if email_result["derivedStatus"] else [0]
sorted_resuls_vector.extend(map(lambda a: a["result"], sorted_rules))
matrix.append(sorted_resuls_vector)
# write matrix to file
out_file = open("../../../web/learning_output.csv", "w")
if out_file:
for row in matrix:
out_file.write(",".join(map(lambda a: str(a), row)))
out_file.write("\n")
out_file.close()
return matrix
def prepare_matrix():
"""
reads results of learning into database and returns them as a matrix
suitable for further processing
Method should be called when database is in consistent state.
Produced matrix has format [M + 1, N + 1]
M is count of emails in database, first two rows contains rule codes.
Firs column of each of M rows contains derived status (1 for phishing, 0 for spam)
Entries [0][0], [1][0] are constant with no practical meaning
Matrix format:
[ '_code' , code1 , code2 ... codeN ]
[ derived_status1 , rule_1_1_result, rule_1_2_result ... rule_1_N_result ]
[ derived_status2 , rule_2_1_result, rule_2_2_result ... rule_2_N_result ]
. . . .
. . . .
. . . .
[ derived_statusM , rule_M_1_result, rule_M_2_result ... rule_M_N_result ]
"""
matrix = []
# indicator of walkthrough
first_loop = True;
for email_id in backend_operations.get_email_ids():
email_result = backend_operations.get_results_of_email(email_id)
if 'rules' in email_result:
#sort rules to ensure same order in all rows of matrix
sorted_rules = sorted(email_result['rules'],key=lambda a: a['code'])
#add first row into matrix (codes) during first walkthrough
if first_loop:
first_row = ['_derived_result']
first_row.extend(map(lambda a: a['code'], sorted_rules))
matrix.append(first_row)
first_loop = False
# append row of matrix
sorted_resuls_vector = [1] if email_result['derivedStatus'] else [0]
sorted_resuls_vector.extend(map(lambda a: a['result'], sorted_rules))
matrix.append(sorted_resuls_vector)
# write matrix to file
out_file = open('../../../web/learning_output.csv','w')
if out_file:
for row in matrix:
out_file.write(','.join(map(lambda a: str(a), row)))
out_file.write('\n')
out_file.close()
return matrix