def logo():
"""
Output our amazing logo
:return: None
"""
print(
bold(
lightyellow(
"""
________ __
\\_____ \\ __ _______ _______| | __
/ / \\ \\| | \\__ \\_ __ \\ |/ /
/ \\_/. \\ | // __ \\| | \\/ <
\\_____\\ \\_/____/(____ /__| |__|_ \\
\\__> \\/ \\/ v{}
"""
),
).format(__version__)
+ bold(
lightblue(
"""
An Obfuscation-Neglect Android Malware Scoring System
"""
),
),
)
def show_label_report(self, rule_path, all_labels, table_version):
"""
Show the report based on label, last column represents max confidence for that label
:param rule_path: the path where may be present the file label_desc.csv.
:param all_labels: dictionary containing label:<array of confidence values associated to that label>
:return: None
"""
label_desc = {}
# clear table to manage max/detail version
self.quark_analysis.label_report_table.clear()
if os.path.isfile(os.path.join(rule_path, "label_desc.csv")):
# associate to each label a description
col_list = ["label", "description"]
# csv file on form <label,description>
# put this file in the folder of rules (it must not be a json file since it could create conflict with management of rules)
df = pd.read_csv(
os.path.join(rule_path, "label_desc.csv"), usecols=col_list
)
label_desc = dict(zip(df["label"], df["description"]))
for label_name in all_labels:
confidences = np.array(all_labels[label_name])
if table_version == "max":
self.quark_analysis.label_report_table.field_names = [
"Label",
"Description",
"Number of rules",
"MAX Confidence %",
]
self.quark_analysis.label_report_table.add_row(
[
green(label_name),
yellow(label_desc.get(label_name, "-")),
(len(confidences)),
red(np.max(confidences)),
]
)
else:
self.quark_analysis.label_report_table.field_names = [
"Label",
"Description",
"Number of rules",
"MAX Confidence %",
"AVG Confidence",
"Std Deviation",
"# of Rules with Confidence >= 80%",
]
self.quark_analysis.label_report_table.add_row(
[
green(label_name),
yellow(label_desc.get(label_name, "-")),
(len(confidences)),
red(np.max(confidences)),
magenta(round(np.mean(confidences), 2)),
lightblue(round(np.std(confidences), 2)),
lightyellow(np.count_nonzero(confidences >= 80)),
]
)
def logo():
print(
bold(
lightyellow("""
________ __
\_____ \ __ _______ _______| | __
/ / \ \| | \__ \\_ __ \ |/ /
/ \_/. \ | // __ \| | \/ <
\_____\ \_/____/(____ /__| |__|_ \\
\__> \/ \/ v{}
""")).format("20.01") + bold(
lightblue("""
An Obfuscation-Neglect Android Malware Scoring System
""")))
