def parsing_commands():
"""
Creation of an ArgumentParser object, holding all the information necessary to parse
the command line into Python data types.
"""
parser = argparse.ArgumentParser(description='Given a list of directory or file paths,\
detects the malicious JS inputs.')
parser.add_argument('--d', metavar='DIR', type=str, nargs='+',
help='directories containing the JS files to be analyzed')
parser.add_argument('--l', metavar='LABEL', type=str, nargs='+',
choices=['benign', 'malicious', '?'],
help='labels of the JS directories to evaluate the model from')
parser.add_argument('--f', metavar='FILE', type=str, nargs='+', help='files to be analyzed')
parser.add_argument('--lf', metavar='LABEL', type=str, nargs='+',
choices=['benign', 'malicious', '?'],
help='labels of the JS files to evaluate the model from')
parser.add_argument('--m', metavar='MODEL', type=str, nargs=1,
help='path of the model used to classify the new JS inputs '
'(see >$ python3 <path-of-clustering/learner.py> -help) '
'to build a model)')
parser.add_argument('--th', metavar='THRESHOLD', type=float, nargs=1, default=[0.50],
help='threshold over which all samples are considered malicious')
utility.parsing_commands(parser)
return vars(parser.parse_args())
def parsing_commands():
"""
Creation of an ArgumentParser object, holding all the information necessary to parse
the command line into Python data types.
"""
parser = argparse.ArgumentParser(
description='Given a list of directory or file paths,\
detects the malicious JS inputs.')
parser.add_argument(
'--d',
metavar='DIR',
type=str,
nargs='+',
help='directories containing the JS files to be analyzed')
parser.add_argument(
'--l',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious', '?'],
help='labels of the JS directories to evaluate the model from')
parser.add_argument('--f',
metavar='FILE',
type=str,
nargs='+',
help='files to be analyzed')
parser.add_argument(
'--lf',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious', '?'],
help='labels of the JS files to evaluate the model from')
parser.add_argument(
'--m',
metavar='MODEL',
type=str,
nargs=1,
help='path of the model used to classify the new JS inputs')
utility.parsing_commands(parser)
return vars(parser.parse_args())
def parsing_commands_clustering():
"""
Creation of an ArgumentParser object, holding all the information necessary to parse
the command line into Python data types.
-------
Returns:
- ArgumentParser such as:
* js_dirs=args['d'],
* js_files=args['f'],
* tolerance=args['t'][0],
* n=args['n'][0],
* nb_cluster=args['c'][0],
* display_fig=args['g'][0],
* labels_d=arg_obj['l'],
* labels_f=arg_obj['lf'].
A more thorough description can be obtained:
>$ python3 <path-of-clustering/cluster.py> -help
"""
parser = argparse.ArgumentParser(description='Given a list of repository or file paths,\
clusters the JS inputs into several families.')
parser.add_argument('--d', metavar='DIR', type=str, nargs='+',
help='directories containing the JS files to be clustered')
parser.add_argument('--f', metavar='FILE', type=str, nargs='+', help='files to be analyzed')
parser.add_argument('--c', metavar='INTEGER', type=int, nargs=1, help='number of clusters')
parser.add_argument('--g', metavar='BOOL', type=bool, nargs=1, default=[False],
help='produces a 2D representation of the files from the JS corpus')
parser.add_argument('--l', metavar='LABEL', type=str, nargs='+', default=None,
help='true labels of the JS directories (used only for display)')
parser.add_argument('--lf', metavar='LABEL', type=str, nargs='+', default=None,
help='true labels of the JS files (used only for display)')
utility.parsing_commands(parser)
return vars(parser.parse_args())
def parsing_commands():
"""
Creation of an ArgumentParser object, holding all the information necessary to parse
the command line into Python data types.
-------
Returns:
- ArgumentParser such as:
* js_dirs=arg_obj['d'],
* labels_d=arg_obj['l'],
* js_files=arg_obj['f'],
* labels_f=arg_obj['lf'],
* old_model=arg_obj['m'][0],
* model_dir=arg_obj['md'][0],
* model_name=arg_obj['mn'][0],
* add_trees=arg_obj['at'][0],
* tolerance=arg_obj['t'][0],
* n=arg_obj['n'][0].
A more thorough description can be obtained:
>$ python3 <path-of-clustering/learner.py> -help
"""
parser = argparse.ArgumentParser(
description='Given a list of directory or file paths, ' +
'updates a model to classify future ' + 'JS inputs.')
parser.add_argument('--d',
metavar='DIR',
type=str,
nargs='+',
help='directories to be used to update a model with')
parser.add_argument(
'--l',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious'],
help='labels of the JS directories used to update a model with')
parser.add_argument('--f',
metavar='FILE',
type=str,
nargs='+',
help='files to be used to update a model with')
parser.add_argument(
'--lf',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious'],
help='labels of the JS files used to update a model with')
parser.add_argument(
'--m',
metavar='OLD-MODEL',
type=str,
nargs=1,
help='path of the old model you wish to update with new JS inputs')
parser.add_argument('--md',
metavar='MODEL-DIR',
type=str,
nargs=1,
default=[os.path.join(src_path, 'Classification')],
help='path to store the model that will be produced')
parser.add_argument('--mn',
metavar='MODEL-NAME',
type=str,
nargs=1,
default=['model'],
help='name of the model that will be produced')
parser.add_argument('--at',
metavar='NB_TREES',
type=int,
nargs=1,
default=[100],
help='number of trees to be added into the forest')
utility.parsing_commands(parser)
return vars(parser.parse_args())
def parsing_commands():
"""
Creation of an ArgumentParser object, holding all the information necessary to parse
the command line into Python data types.
-------
Returns:
- ArgumentParser such as:
* js_dirs=arg_obj['d'],
* labels_d=arg_obj['l'],
* js_files=arg_obj['f'],
* labels_f=arg_obj['lf'],
* model_dir=arg_obj['md'][0],
* model_name=arg_obj['mn'][0],
* print_score=arg_obj['ps'][0],
* print_res=arg_obj['pr'][0],
* estimators=arg_obj['nt'][0],
* tolerance=arg_obj['t'][0],
* n=arg_obj['n'][0].
A more thorough description can be obtained:
>$ python3 <path-of-clustering/learner.py> -help
"""
parser = argparse.ArgumentParser(
description='Given a list of directory or file paths, ' +
'builds a model to classify future ' + 'JS inputs.')
parser.add_argument('--d',
metavar='DIR',
type=str,
nargs='+',
help='directories to be used to build a model from')
parser.add_argument(
'--l',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious'],
help='labels of the JS directories used to build a model from')
parser.add_argument('--f',
metavar='FILE',
type=str,
nargs='+',
help='files to be used to build a model from')
parser.add_argument(
'--lf',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious'],
help='labels of the JS files used to build a model from')
parser.add_argument('--md',
metavar='MODEL-DIR',
type=str,
nargs=1,
default=[os.path.join(src_path, 'Classification')],
help='path to store the model that will be produced')
parser.add_argument('--mn',
metavar='MODEL-NAME',
type=str,
nargs=1,
default=['model'],
help='name of the model that will be produced')
parser.add_argument(
'--ps',
metavar='BOOL',
type=bool,
nargs=1,
default=[False],
help=
'indicates whether to print or not the classifier\'s detection rate')
parser.add_argument(
'--pr',
metavar='BOOL',
type=bool,
nargs=1,
default=[False],
help='indicates whether to print or not the classifier\'s predictions')
parser.add_argument('--nt',
metavar='NB_TREES',
type=int,
nargs=1,
default=[500],
help='number of trees in the forest')
utility.parsing_commands(parser)
return vars(parser.parse_args())
def parsing_commands():
"""
Creation of an ArgumentParser object, holding all the information necessary to parse
the command line into Python data types.
"""
parser = argparse.ArgumentParser(
description='Given a list of directory or file paths, '
'builds a model to classify future '
'JS inputs.')
parser.add_argument('--d',
metavar='DIR',
type=str,
nargs='+',
help='directories to be used to build a model from')
parser.add_argument(
'--l',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious'],
help='labels of the JS directories used to build a model from')
parser.add_argument(
'--vd',
metavar='DIR-VALIDATE',
type=str,
nargs='+',
help='2 JS dir (1 benign, 1 malicious) to select the features with chi2'
)
parser.add_argument(
'--vl',
metavar='LABEL',
type=str,
nargs='+',
choices=['benign', 'malicious'],
help='labels of the 2 JS dir for the features selection process')
parser.add_argument('--md',
metavar='MODEL-DIR',
type=str,
nargs=1,
default=[os.path.join(SRC_PATH, 'Analysis')],
help='path to store the model that will be produced')
parser.add_argument('--mn',
metavar='MODEL-NAME',
type=str,
nargs=1,
default=['model'],
help='name of the model that will be produced')
parser.add_argument(
'--ps',
metavar='BOOL',
type=bool,
nargs=1,
default=[False],
help=
'indicates whether to print or not the classifier\'s detection rate')
parser.add_argument(
'--pr',
metavar='BOOL',
type=bool,
nargs=1,
default=[False],
help='indicates whether to print or not the classifier\'s predictions')
parser.add_argument('--nt',
metavar='NB_TREES',
type=int,
nargs=1,
default=[500],
help='number of trees in the forest')
utility.parsing_commands(parser)
return vars(parser.parse_args())