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195 | from collections import OrderedDict
VERSION = "1.1.0"
"""Version of this set of plugin definitions."""
MINIMUM_THAT_VERSION = "1.1.0"
"""Minimum THAT version required to run these plugin definitions."""
NAME = "trendmicro"
PRIORITY = 5
ANALYZE_DATA = OrderedDict()
GET_TANIUM_DATA = OrderedDict()
GET_INTERNET_DATA = [
]
# ask the question 'Get Installed Applications contains "trend micro" from all machines'
# and store results in "trendmicro.csv"
GET_TANIUM_DATA["trendmicro.csv"] = {
"filters": [],
"sensors": ["Installed Applications, that contains:trend micro"],
}
# ask the question 'Get Online from all machines'
# and store results in "trendmicrotargets.csv"
GET_TANIUM_DATA["trendmicrotargets.csv"] = {
"filters": ["Is Windows, that contains:True"],
"sensors": ["Online"],
}
# ask the question 'Get Trend Micro Client Version from all machines'
# and store results in "trendversion.csv"
GET_TANIUM_DATA["trendversion.csv"] = {
"filters": [],
"sensors": ["Trend Micro Client Version"],
}
# ask the question 'Get Trend Micro Pattern Days Old from all machines'
# and store results in "trendpatdaysold.csv"
GET_TANIUM_DATA["trendpatdaysold.csv"] = {
"filters": [],
"sensors": ["Trend Micro Pattern Days Old"],
}
# ask the question 'Get Trend Micro Pattern Version from all machines'
# and store results in "trendpatver.csv"
GET_TANIUM_DATA["trendpatver.csv"] = {
"filters": [],
"sensors": ["Trend Micro Pattern Version"],
}
ANALYZE_DATA["cleaned_trend_df"] = """
# clean out noise from trendmicro.csv
csv = "trendmicro.csv"
df = self.load_csv_as_df(csv)
result = self.clean_df(df, columns=["Name"])
"""
ANALYZE_DATA["cleaned_trendversion_df"] = """
# clean out noise from trendversion.csv
csv = "trendversion.csv"
df = self.load_csv_as_df(csv)
result = self.clean_df(df, columns=["Trend Micro Client Version"])
"""
ANALYZE_DATA["cleaned_trendpatdaysold_df"] = """
# clean out noise from trendpatdaysold.csv
csv = "trendpatdaysold.csv"
df = self.load_csv_as_df(csv)
result = self.clean_df(df, columns=["Trend Micro Pattern Days Old"])
"""
ANALYZE_DATA["cleaned_trendpatver_df"] = """
# clean out noise from trendpatver.csv
csv = "trendpatver.csv"
df = self.load_csv_as_df(csv)
result = self.clean_df(df, columns=["Trend Micro Pattern Version"])
"""
ANALYZE_DATA["trendmicro_targets"] = """
# Total Number of Scanned Endpoints for TrendMicro Products
csv = "trendmicrotargets.csv"
df = self.load_csv_as_df(csv)
result = self.force_int(df['Count'].sum(), 0)
"""
ANALYZE_DATA["product_count"] = """
# get total count of TrendMicro products installed across endpoints
df = self.get_result("cleaned_trend_df")
result = self.force_int(len(df['Name'].unique()), 0)
"""
ANALYZE_DATA["install_count"] = """
# get total number of times TrendMicro products installed across all endpoints
df = self.get_result("cleaned_trend_df")
result = self.force_int(df['Count'].sum(), 0)
"""
ANALYZE_DATA["install_avg"] = """
# average TrendMicro installs per endpoint
trendmicro_targets = self.get_result("trendmicro_targets")
install_count = self.get_result("install_count")
if trendmicro_targets > 0:
if (float(install_count) / float(trendmicro_targets)) < 1:
result = math.ceil(float(install_count) / float(trendmicro_targets))
else:
result = float(install_count) / float(trendmicro_targets)
else:
result = 0
result = self.force_int(int(result), 0)
"""
ANALYZE_DATA["not_installed"] = """
# total number endpoints missing TrendMicro (Not Installed)
df = self.get_result("cleaned_trendversion_df")
col = 'Trend Micro Client Version'
result = self.force_int(df.loc[df[col] == 'Not installed', 'Count'].sum(), 0)
"""
ANALYZE_DATA["versions_installed"] = """
# number of TrendMicro Versions Detected
df = self.get_result("cleaned_trendversion_df")
col = 'Trend Micro Client Version'
result = self.force_int(len(df.loc[df[col] != 'Not installed']), 0)
"""
ANALYZE_DATA["oldest_pat_days"] = """
# TrendMicro Patterns Days Old (Oldest)
df = self.get_result("cleaned_trendpatdaysold_df")
col = 'Trend Micro Pattern Days Old'
val = df.loc[df[col] != 'Not installed', col].max()
result = self.force_int(val, 0) if val is not numpy.nan else "N/A"
"""
ANALYZE_DATA["pat_versions"] = """
# Number of Detected TrendMicro Pattern Versions
df = self.get_result("cleaned_trendpatver_df")
col = 'Trend Micro Pattern Version'
result = self.force_int(len(df.loc[df[col] != 'Not installed']), 0)
"""
ANALYZE_DATA["versions_installed_df"] = """
# Data frame with all versions installed
df = self.get_result("cleaned_trendpatver_df")
col = 'Trend Micro Pattern Version'
result = df.loc[df[col] != 'Not installed']
"""
ANALYZE_DATA["newest_version"] = """
# Latest version of version installed
df = self.get_result("versions_installed_df")
col = 'Trend Micro Pattern Version'
val = df[col].max()
result = val if val is not numpy.nan else "N/A"
"""
ANALYZE_DATA["old_pat_versions_ep"] = """
# Number of Endpoints with "older" TrendMicro Pattern Versions
df = self.get_result("versions_installed_df")
newest_ver = self.get_result("newest_version")
col = 'Trend Micro Pattern Version'
result = self.force_int(df.loc[df[col] != newest_ver, 'Count'].sum(), 0)
"""
|