Cyberdefense/VulnWhisperer
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Create cvss score from base and temporal

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This commit is contained in:
pemontto
2019-04-15 13:32:31 +10:00
parent 603050e7b3
commit 275b89c94d
4 changed files with 81 additions and 80 deletions
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75
vulnwhisp/frameworks/qualys_vuln.py
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@ -29,7 +29,7 @@ class qualysWhisperAPI(object):
def scan_xml_parser(self, xml):
all_records = []
root = ET.XML(xml.encode("utf-8"))
root = ET.XML(xml.encode('utf-8'))
for child in root.find('.//SCAN_LIST'):
all_records.append({
'name': child.find('TITLE').text,
@ -81,8 +81,6 @@ class qualysVulnScan:
COLUMN_MAPPING = {
'cve_id': 'cve',
'cvss_base': 'cvss',
'cvss3_base': 'cvss3',
'impact': 'synopsis',
'ip_status': 'state',
'os': 'operating_system',
@ -137,77 +135,80 @@ class qualysVulnScan:
return scan_report
def normalise(self, dataframe):
def normalise(self, df):
self.logger.debug('Normalising data')
self.map_fields(dataframe)
self.transform_values(dataframe)
return dataframe
df = self.map_fields(df)
df = self.transform_values(df)
return df
def map_fields(self, dataframe):
def map_fields(self, df):
self.logger.info('Mapping fields')
# Map fields from COLUMN_MAPPING
fields = [x.lower() for x in dataframe.columns]
fields = [x.lower() for x in df.columns]
for field, replacement in self.COLUMN_MAPPING.iteritems():
if field in fields:
self.logger.info('Renaming "{}" to "{}"'.format(field, replacement))
fields[fields.index(field)] = replacement
fields = [x.replace(' ', '_') for x in fields]
dataframe.columns = fields
df.columns = fields
return dataframe
return df
def transform_values(self, dataframe):
def transform_values(self, df):
self.logger.info('Transforming values')
# upper/lowercase fields
self.logger.info('Changing case of fields')
dataframe['cve'] = dataframe['cve'].str.upper()
dataframe['protocol'] = dataframe['protocol'].str.lower()
df['cve'] = df['cve'].str.upper()
df['protocol'] = df['protocol'].str.lower()
# Contruct the CVSS vector
dataframe['cvss_vector'] = ''
dataframe.loc[dataframe["cvss"].notnull(), "cvss_vector"] = (
dataframe.loc[dataframe["cvss"].notnull(), "cvss"]
df['cvss_vector'] = ''
df.loc[df['cvss_base'].notnull(), 'cvss_vector'] = (
df.loc[df['cvss_base'].notnull(), 'cvss_base']
.str.split()
.apply(lambda x: x[1])
.str.replace("(", "")
.str.replace(")", "")
.str.replace('(', '')
.str.replace(')', '')
)
dataframe.loc[dataframe["cvss"].notnull(), "cvss"] = (
dataframe.loc[dataframe["cvss"].notnull(), "cvss"]
df.loc[df['cvss_base'].notnull(), 'cvss_base'] = (
df.loc[df['cvss_base'].notnull(), 'cvss_base']
.str.split()
.apply(lambda x: x[0])
)
dataframe['cvss_temporal_vector'] = ''
dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal_vector"] = (
dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal"]
df['cvss_temporal_vector'] = ''
df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal_vector'] = (
df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal']
.str.split()
.apply(lambda x: x[1])
.str.replace("(", "")
.str.replace(")", "")
.str.replace('(', '')
.str.replace(')', '')
)
dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal"] = (
dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal"]
df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal'] = (
df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal']
.str.split()
.apply(lambda x: x[0])
.fillna('')
)
# Combine base and temporal
dataframe["cvss_vector"] = (
dataframe[["cvss_vector", "cvss_temporal_vector"]]
.apply(lambda x: "{}/{}".format(x[0], x[1]), axis=1)
.str.rstrip("/nan")
.fillna("")
df['cvss_vector'] = (
df[['cvss_vector', 'cvss_temporal_vector']]
.apply(lambda x: '{}/{}'.format(x[0], x[1]), axis=1)
.str.rstrip('/nan')
.fillna('')
)
dataframe.drop('cvss_temporal_vector', axis=1, inplace=True)
df.drop('cvss_temporal_vector', axis=1, inplace=True)
# Convert Qualys severity to standardised risk number
dataframe['risk_number'] = dataframe['severity'].astype(int)-1
df['risk_number'] = df['severity'].astype(int)-1
dataframe.fillna('', inplace=True)
df['cvss'] = df['cvss_base']
df.loc[df['cvss_temporal'].notnull(), 'cvss'] = df['cvss_temporal']
return dataframe
df.fillna('', inplace=True)
return df