cli/streamingphish/streamingphish/features.py
def _fe_compute_domain_entropy(sample):
        """
        Takes domain name from FQDN and computes entropy (randomness, repeated characters, etc).

        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.
        """
        # Compute entropy of domain.
        result = OrderedDict()
        p, lns = Counter(sample['domain']), float(len(sample['domain']))
        entropy = -sum(count / lns * math.log(count / lns, 2) for count in list(p.values()))

        result['entropy'] = entropy
        return result

        Takes domain name from FQDN and computes entropy (randomness, repeated characters, etc).

        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.
        
Takes domain name from FQDN and computes entropy (randomness, repeated characters, etc).
Takes domain name from FQDN and computes entropy ( randomness repeated characters etc ) .
def _fe_compute_domain_entropy(sample):
        result = OrderedDict()
        p, lns = Counter(sample['domain']), float(len(sample['domain']))
        entropy = -sum(count / lns * math.log(count / lns, 2) for count in list(p.values()))
        result['entropy'] = entropy
        return result
def _fe_compute_domain_entropy ( sample ) : result = OrderedDict ( ) p , lns = Counter ( sample [ 'domain' ] ) , float ( len ( sample [ 'domain' ] ) ) entropy = - sum ( count / lns * math . log ( count / lns , 2 ) for count in list ( p . values ( ) ) ) result [ 'entropy' ] = entropy return result
Takes domain name from FQDN and computes entropy (randomness, repeated characters, etc).
Takes domain name from FQDN and computes entropy ( randomness repeated characters etc ) .
"""
        Takes domain name from FQDN and computes entropy (randomness, repeated characters, etc).

        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.
        """ # Compute entropy of domain.
result (dictionary): Keys are feature names, values are feature scores.
result ( dictionary ) : Keys are feature names values are feature scores .
sample
Info about the sample being analyzed i.e. subdomain, tld, fqdn
Info about the sample being analyzed i . e . subdomain tld fqdn