Create cvss score from base and temporal
This commit is contained in:
@ -25,10 +25,10 @@ class NessusAPI(object):
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EXPORT_HISTORY = EXPORT + '?history_id={history_id}'
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# All column mappings should be lowercase
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COLUMN_MAPPING = {
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'cvss base score': 'cvss',
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'cvss base score': 'cvss_base',
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'cvss temporal score': 'cvss_temporal',
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'cvss temporal vector': 'cvss_temporal_vector',
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'cvss3 base score': 'cvss3',
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'cvss3 base score': 'cvss3_base',
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'cvss3 temporal score': 'cvss3_temporal',
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'cvss3 temporal vector': 'cvss3_temporal_vector',
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'fqdn': 'dns',
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@ -188,64 +188,64 @@ class NessusAPI(object):
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'None': 'US/Central'}
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return time_map.get(tz, None)
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def normalise(self, dataframe):
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def normalise(self, df):
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self.logger.debug('Normalising data')
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self.map_fields(dataframe)
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self.transform_values(dataframe)
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return dataframe
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df = self.map_fields(df)
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df = self.transform_values(df)
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return df
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def map_fields(self, dataframe):
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def map_fields(self, df):
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self.logger.debug('Mapping fields')
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# Any specific mappings here
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if self.profile == 'tenable':
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# Prefer CVSS Base Score over CVSS for tenable
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self.logger.debug('Dropping redundant tenable fields')
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dataframe.drop('CVSS', axis=1, inplace=True)
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dataframe.drop('IP Address', axis=1, inplace=True)
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df.drop('CVSS', axis=1, inplace=True)
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df.drop('IP Address', axis=1, inplace=True)
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# Map fields from COLUMN_MAPPING
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fields = [x.lower() for x in dataframe.columns]
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fields = [x.lower() for x in df.columns]
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for field, replacement in self.COLUMN_MAPPING.iteritems():
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if field in fields:
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self.logger.debug('Renaming "{}" to "{}"'.format(field, replacement))
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fields[fields.index(field)] = replacement
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fields = [x.replace(' ', '_') for x in fields]
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dataframe.columns = fields
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df.columns = fields
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return df
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return dataframe
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def transform_values(self, dataframe):
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def transform_values(self, df):
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self.logger.debug('Transforming values')
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# upper/lowercase fields
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self.logger.debug('Changing case of fields')
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dataframe['cve'] = dataframe['cve'].str.upper()
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dataframe['protocol'] = dataframe['protocol'].str.lower()
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df['cve'] = df['cve'].str.upper()
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df['protocol'] = df['protocol'].str.lower()
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# Copy asset to IP
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dataframe['ip'] = dataframe['asset']
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df['ip'] = df['asset']
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# Map risk to a SEVERITY MAPPING value
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self.logger.debug('Mapping risk to severity number')
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dataframe['risk_number'] = dataframe['risk'].str.lower()
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dataframe['risk_number'].replace(self.SEVERITY_MAPPING, inplace=True)
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df['risk_number'] = df['risk'].str.lower()
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df['risk_number'].replace(self.SEVERITY_MAPPING, inplace=True)
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if self.profile == 'tenable':
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self.logger.debug('Combinging CVSS vectors for tenable')
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# Combine CVSS vectors
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dataframe['cvss_vector'] = (
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dataframe[['cvss_vector', 'cvss_temporal_vector']]
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df['cvss_vector'] = (
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df[['cvss_vector', 'cvss_temporal_vector']]
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.apply(lambda x: '{}/{}'.format(x[0], x[1]), axis=1)
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.str.rstrip('/nan')
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)
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dataframe['cvss3_vector'] = (
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dataframe[['cvss3_vector', 'cvss3_temporal_vector']]
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df['cvss3_vector'] = (
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df[['cvss3_vector', 'cvss3_temporal_vector']]
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.apply(lambda x: '{}/{}'.format(x[0], x[1]), axis=1)
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.str.rstrip('/nan')
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)
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# CVSS score = cvss_temporal if cvss_temporal else cvss_base
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df['cvss'] = df['cvss_base']
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df.loc[df['cvss_temporal'].notnull(), 'cvss'] = df['cvss_temporal']
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dataframe.fillna('', inplace=True)
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return dataframe
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df.fillna('', inplace=True)
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return df
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@ -191,16 +191,16 @@ class OpenVAS_API(object):
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merged_df = pd.merge(report_df, self.openvas_reports, on='report_ids').reset_index().drop('index', axis=1)
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return merged_df
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def normalise(self, dataframe):
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def normalise(self, df):
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self.logger.debug('Normalising data')
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self.map_fields(dataframe)
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self.transform_values(dataframe)
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return dataframe
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df = self.map_fields(df)
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df = self.transform_values(df)
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return df
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def map_fields(self, dataframe):
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def map_fields(self, df):
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self.logger.debug('Mapping fields')
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return dataframe
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return df
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def transform_values(self, dataframe):
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def transform_values(self, df):
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self.logger.debug('Transforming values')
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return dataframe
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return df
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@ -29,7 +29,7 @@ class qualysWhisperAPI(object):
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def scan_xml_parser(self, xml):
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all_records = []
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root = ET.XML(xml.encode("utf-8"))
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root = ET.XML(xml.encode('utf-8'))
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for child in root.find('.//SCAN_LIST'):
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all_records.append({
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'name': child.find('TITLE').text,
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@ -81,8 +81,6 @@ class qualysVulnScan:
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COLUMN_MAPPING = {
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'cve_id': 'cve',
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'cvss_base': 'cvss',
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'cvss3_base': 'cvss3',
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'impact': 'synopsis',
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'ip_status': 'state',
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'os': 'operating_system',
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@ -137,77 +135,80 @@ class qualysVulnScan:
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return scan_report
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def normalise(self, dataframe):
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def normalise(self, df):
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self.logger.debug('Normalising data')
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self.map_fields(dataframe)
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self.transform_values(dataframe)
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return dataframe
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df = self.map_fields(df)
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df = self.transform_values(df)
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return df
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def map_fields(self, dataframe):
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def map_fields(self, df):
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self.logger.info('Mapping fields')
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# Map fields from COLUMN_MAPPING
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fields = [x.lower() for x in dataframe.columns]
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fields = [x.lower() for x in df.columns]
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for field, replacement in self.COLUMN_MAPPING.iteritems():
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if field in fields:
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self.logger.info('Renaming "{}" to "{}"'.format(field, replacement))
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fields[fields.index(field)] = replacement
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fields = [x.replace(' ', '_') for x in fields]
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dataframe.columns = fields
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df.columns = fields
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return dataframe
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return df
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def transform_values(self, dataframe):
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def transform_values(self, df):
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self.logger.info('Transforming values')
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# upper/lowercase fields
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self.logger.info('Changing case of fields')
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dataframe['cve'] = dataframe['cve'].str.upper()
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dataframe['protocol'] = dataframe['protocol'].str.lower()
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df['cve'] = df['cve'].str.upper()
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df['protocol'] = df['protocol'].str.lower()
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# Contruct the CVSS vector
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dataframe['cvss_vector'] = ''
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dataframe.loc[dataframe["cvss"].notnull(), "cvss_vector"] = (
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dataframe.loc[dataframe["cvss"].notnull(), "cvss"]
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df['cvss_vector'] = ''
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df.loc[df['cvss_base'].notnull(), 'cvss_vector'] = (
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df.loc[df['cvss_base'].notnull(), 'cvss_base']
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.str.split()
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.apply(lambda x: x[1])
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.str.replace("(", "")
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.str.replace(")", "")
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.str.replace('(', '')
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.str.replace(')', '')
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)
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dataframe.loc[dataframe["cvss"].notnull(), "cvss"] = (
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dataframe.loc[dataframe["cvss"].notnull(), "cvss"]
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df.loc[df['cvss_base'].notnull(), 'cvss_base'] = (
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df.loc[df['cvss_base'].notnull(), 'cvss_base']
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.str.split()
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.apply(lambda x: x[0])
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)
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dataframe['cvss_temporal_vector'] = ''
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dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal_vector"] = (
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dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal"]
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df['cvss_temporal_vector'] = ''
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df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal_vector'] = (
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df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal']
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.str.split()
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.apply(lambda x: x[1])
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.str.replace("(", "")
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.str.replace(")", "")
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.str.replace('(', '')
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.str.replace(')', '')
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)
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dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal"] = (
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dataframe.loc[dataframe["cvss_temporal"].notnull(), "cvss_temporal"]
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df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal'] = (
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df.loc[df['cvss_temporal'].notnull(), 'cvss_temporal']
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.str.split()
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.apply(lambda x: x[0])
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.fillna('')
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)
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# Combine base and temporal
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dataframe["cvss_vector"] = (
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dataframe[["cvss_vector", "cvss_temporal_vector"]]
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.apply(lambda x: "{}/{}".format(x[0], x[1]), axis=1)
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.str.rstrip("/nan")
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.fillna("")
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df['cvss_vector'] = (
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df[['cvss_vector', 'cvss_temporal_vector']]
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.apply(lambda x: '{}/{}'.format(x[0], x[1]), axis=1)
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.str.rstrip('/nan')
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.fillna('')
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)
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dataframe.drop('cvss_temporal_vector', axis=1, inplace=True)
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df.drop('cvss_temporal_vector', axis=1, inplace=True)
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# Convert Qualys severity to standardised risk number
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dataframe['risk_number'] = dataframe['severity'].astype(int)-1
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df['risk_number'] = df['severity'].astype(int)-1
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dataframe.fillna('', inplace=True)
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df['cvss'] = df['cvss_base']
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df.loc[df['cvss_temporal'].notnull(), 'cvss'] = df['cvss_temporal']
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return dataframe
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df.fillna('', inplace=True)
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return df
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@ -464,16 +464,16 @@ class qualysScanReport:
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return merged_data
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def normalise(self, dataframe):
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def normalise(self, df):
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self.logger.debug('Normalising data')
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self.map_fields(dataframe)
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self.transform_values(dataframe)
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return dataframe
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df = self.map_fields(df)
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df = self.transform_values(df)
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return df
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def map_fields(self, dataframe):
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def map_fields(self, df):
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self.logger.debug('Mapping fields')
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return dataframe
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return df
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def transform_values(self, dataframe):
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def transform_values(self, df):
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self.logger.debug('Transforming values')
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return dataframe
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return df
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