cli/streamingphish/streamingphish/features.py
def _fe_keyword_match_fqdn_words(self, sample):
        """
        Compare FQDN words (previous regex on special characters) against a list of common
        phishing keywords, look for exact match on those words. Probably more decisive
        in identifying phishing domains.

        Args:
            sample (dictionary): Info about the sample being analyzed i.e. subdomain, tld, fqdn

        Returns:
            result (dictionary): Keys are feature names, values are feature scores.
        """
        result = OrderedDict()

        for item in self._fqdn_keywords:
            result[item + "_kw_fqdn_words"] = 1 if item in sample['fqdn_words'] else 0

        return result

        Compare FQDN words (previous regex on special characters) against a list of common
        phishing keywords, look for exact match on those words. Probably more decisive
        in identifying phishing domains.

        Args:
            sample (dictionary): Info about the sample being analyzed i.e. subdomain, tld, fqdn

        Returns:
            result (dictionary): Keys are feature names, values are feature scores.
        
Compare FQDN words (previous regex on special characters) against a list of common
phishing keywords, look for exact match on those words. Probably more decisive
in identifying phishing domains.
Compare FQDN words ( previous regex on special characters ) against a list of common phishing keywords look for exact match on those words . Probably more decisive in identifying phishing domains .
def _fe_keyword_match_fqdn_words(self, sample):
        result = OrderedDict()
        for item in self._fqdn_keywords:
            result[item + "_kw_fqdn_words"] = 1 if item in sample['fqdn_words'] else 0
        return result
def _fe_keyword_match_fqdn_words ( self , sample ) : result = OrderedDict ( ) for item in self . _fqdn_keywords : result [ item + "_kw_fqdn_words" ] = 1 if item in sample [ 'fqdn_words' ] else 0 return result
Compare FQDN words (previous regex on special characters) against a list of common
phishing keywords, look for exact match on those words.
Compare FQDN words ( previous regex on special characters ) against a list of common phishing keywords look for exact match on those words .
"""
        Compare FQDN words (previous regex on special characters) against a list of common
        phishing keywords, look for exact match on those words. Probably more decisive
        in identifying phishing domains.

        Args:
            sample (dictionary): Info about the sample being analyzed i.e. subdomain, tld, fqdn

        Returns:
            result (dictionary): Keys are feature names, values are feature scores.
        """
result (dictionary): Keys are feature names, values are feature scores.
result ( dictionary ) : Keys are feature names values are feature scores .
self
sample
Info about the sample being analyzed i.e. subdomain, tld, fqdn
Info about the sample being analyzed i . e . subdomain tld fqdn