Upload cybersecurity_attacks.py

cybersecurity_attacks.py

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+# -*- coding: utf-8 -*-
+"""Cybersecurity Attacks
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1IaRXrO_gtRMKaHU7_IPzZJ705GonLuMV
+"""
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import seaborn as sns
+import matplotlib.pyplot as plt
+import re
+
+pd.options.display.float_format = '{:,.2f}'.format
+
+df = pd.read_csv("/content/cybersecurity_attacks.csv")
+
+print(f"There are {df.shape[0]}, row and {df.shape[1]} columns in the dataset")
+
+df.columns
+
+df.isna().sum()
+
+df.duplicated().sum()
+
+df['Malware Indicators'] = df['Malware Indicators'].apply(lambda x: 'No Detection' if pd.isna(x) else x)
+
+df['Alerts/Warnings'] = df['Alerts/Warnings'].apply(lambda x: 'yes' if x == 'Alert Triggered' else 'no')
+
+df['Prowx Information'] = df['Proxy Information'].apply(lambda x: 'No proxy' if pd.isna(x) else x)
+
+df['Firewall Logs'] = df['Firewall Logs'].apply(lambda x: 'No Data' if pd.isna(x) else x)
+
+df['IDS/IPS Alerts'] = df['IDS/IPS Alerts'].apply(lambda x: 'No Data' if pd.isna(x) else x)
+
+df['Source Port'] = df['Source Port'].apply(lambda x: x if 0 <= x <= 65535 else 'Invalid Port')
+df['Destination Port'] = df['Destination Port'].apply(lambda x: x if 0 <= x <= 65535 else 'Invalid Port')
+
+df.isna().sum()
+
+df['Device Information'].value_counts()
+
+df['Browser'] = df['Device Information'].str.split('/').str[0]
+
+df['Browser'].unique()
+
+patterns = [
+    r'Windows',
+    r'Linux',
+    r'Android',
+    r'iPad',
+    r'iPhone',
+    r'Macintosh',
+]
+
+def extract_device_or_os(user_agent):
+  for pattern in patterns:
+    match = re.search(pattern, user_agent, re.I)
+    if match:
+      return match.group()
+
+    return 'Unknown'
+
+df['Device/OS'] = df['Device Information'].apply(extract_device_or_os)
+
+df = df.drop('Device Information', axis = 1)
+
+device_counts = df['Device/OS'].value_counts()
+
+device_counts_df = pd.DataFrame(device_counts).reset_index()
+device_counts_df.columns = ['Device/OS', 'Count of Attacks']
+
+top_devices = device_counts_df.head(10)
+print(top_devices)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Count of Attacks', y='Device/OS', hue='Device/OS', data=top_devices, palette='viridis', dodge=False)
+plt.xlabel('Count of Attacks')
+plt.ylabel('Device/OS')
+plt.title('Top 10 Devices/OS Targeted by Attacks')
+plt.tight_layout()
+plt.show()
+
+attack_type_counts = df['Attack Type'].value_counts()
+attack_type_counts_df = pd.DataFrame(attack_type_counts).reset_index()
+attack_type_counts_df.columns = ['Attack Type', 'Count of Attacks']
+top_attack_types = attack_type_counts_df.head(10)
+print(top_attack_types)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Count of Attacks', y='Attack Type', hue='Attack Type', data=top_attack_types, palette='viridis', dodge=False)
+plt.xlabel('Count of Attacks')
+plt.ylabel('Attack Type')
+plt.title('Top 10 Most Common Attack Types')
+plt.tight_layout()
+plt.show()
+
+geo_location_counts = df['Geo-location Data'].value_counts()
+
+geo_location_counts_df = pd.DataFrame(geo_location_counts).reset_index()
+geo_location_counts_df.columns = ['Geo-location Data', 'Count of Attacks']
+top_geo_locations = geo_location_counts_df.head(10)
+print(top_geo_locations)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Count of Attacks', y='Geo-location Data', hue='Geo-location Data', data=top_geo_locations, palette='viridis', dodge=False)
+plt.xlabel('Count of Attacks')
+plt.ylabel('Geo-location Data')
+plt.title('Top 10 Geographic Locations Source of Malicious Traffic')
+plt.tight_layout()
+plt.show()
+
+destination_port_counts = df['Destination Port'].value_counts()
+destination_port_counts_df = pd.DataFrame(destination_port_counts).reset_index()
+destination_port_counts_df.columns = ['Destination Port', 'Count of Attacks']
+
+top_destination_ports = destination_port_counts_df.head(10)
+print(top_destination_ports)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Destination Port', y='Count of Attacks', hue='Count of Attacks', data=top_destination_ports, palette='viridis', dodge=False)
+plt.xlabel('Destination Port')
+plt.ylabel('Count of Attacks')
+plt.title('Top 10 Most Targeted Destination Ports')
+plt.tight_layout()
+plt.show()
+
+severity_mapping = {'Low': 1, 'Medium': 2, 'High': 3}
+df['Severity Level Numeric'] = df['Severity Level'].map(severity_mapping)
+
+protocol_severity = df.groupby('Protocol')['Severity Level Numeric'].mean().reset_index()
+
+protocol_severity = protocol_severity.sort_values(by='Severity Level Numeric', ascending=False)
+
+top_protocol_severity = protocol_severity.head(10)
+print(top_protocol_severity)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Severity Level Numeric', y='Protocol', hue='Severity Level Numeric', data=top_protocol_severity, palette='viridis', dodge=False)
+plt.xlabel('Mean Severity Level')
+plt.ylabel('Protocol')
+plt.title('Top 10 Protocols by Mean Severity Level')
+plt.tight_layout()
+plt.show()
+
+df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
+df['Month'] = df['Timestamp'].dt.month
+month_counts = df['Month'].value_counts()
+month_counts_df = pd.DataFrame(month_counts).reset_index()
+month_counts_df.columns = ['Month', 'Count of Attacks']
+sorted_month_counts = month_counts_df.sort_values(by='Month')
+print(sorted_month_counts)
+
+df['Timestamp'] = pd.to_datetime(df['Timestamp'])
+df['Month'] = df['Timestamp'].dt.month
+
+attacks_by_month = df.groupby('Month').size().reset_index(name='Attack Count')
+
+heatmap_data = attacks_by_month.pivot_table(index='Month', columns='Month', values='Attack Count', aggfunc='sum', fill_value=0)
+plt.figure(figsize=(10, 6))
+sns.heatmap(heatmap_data, annot=True, fmt='d', cmap='YlOrRd', cbar=True)
+plt.title('Cybersecurity Attacks Frequency by Month')
+plt.xlabel('Month')
+plt.ylabel('Month')
+plt.tight_layout()
+plt.show()
+
+malicious_traffic = df[df['Malware Indicators'] == 'IoCDetected']
+
+traffic_type_counts = malicious_traffic['Traffic Type'].value_counts()
+traffic_type_counts_df = pd.DataFrame(traffic_type_counts).reset_index()
+traffic_type_counts_df.columns = ['Traffic Type', 'Count of Malicious Incidents']
+
+top_traffic_types = traffic_type_counts_df.head(10)
+print(top_traffic_types)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Count of Malicious Incidents', y='Traffic Type', hue='Count of Malicious Incidents', data=top_traffic_types, palette='viridis', dodge=False)
+plt.xlabel('Count of Malicious Incidents')
+plt.ylabel('Traffic Type')
+plt.title('Top Traffic Types Flagged with "IoC Detected"')
+plt.tight_layout()
+plt.show()
+
+threshold = 75.0
+
+infiltration_data = df[df['Anomaly Scores'] > threshold]
+
+vulnerable_traffic_counts = infiltration_data['Traffic Type'].value_counts()
+
+vulnerable_traffic_df = pd.DataFrame(vulnerable_traffic_counts).reset_index()
+vulnerable_traffic_df.columns = ['Traffic Type', 'Count of Infiltrations']
+
+top_vulnerable_traffic = vulnerable_traffic_df.head(10)
+print(top_vulnerable_traffic)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Count of Infiltrations', y='Traffic Type', hue='Count of Infiltrations', data=top_vulnerable_traffic, dodge=False)
+plt.xlabel('Count of Infiltrations')
+plt.ylabel('Traffic Type')
+plt.title('Top Traffic Types Vulnerable to Infiltration (Anomaly Scores)')
+plt.tight_layout()
+plt.show()
+
+threshold = df['Anomaly Scores'].quantile(0.95)
+print(f"Threshold for Anomaly Scores: {threshold}\n")
+
+infiltration_data = df[df['Anomaly Scores'] > threshold]
+
+print(infiltration_data.head())
+
+vulnerable_traffic_counts = infiltration_data['Traffic Type'].value_counts()
+vulnerable_traffic_df = pd.DataFrame(vulnerable_traffic_counts).reset_index()
+vulnerable_traffic_df.columns = ['Traffic Type', 'Count of Infiltrations']
+
+top_vulnerable_traffic = vulnerable_traffic_df.head(10)
+print(top_vulnerable_traffic)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Count of Infiltrations', y='Traffic Type', hue='Count of Infiltrations', data=top_vulnerable_traffic, palette='viridis', dodge=False)
+plt.xlabel('Count of Infiltrations')
+plt.ylabel('Traffic Type')
+plt.title('Top Traffic Types Vulnerable to Infiltration (High Anomaly Scores)')
+plt.tight_layout()
+plt.show()
+
+cyber_attacks_data = df[df['Action Taken'].str.contains('Blocked', case=False, na=False)]
+
+vulnerable_devices_os_counts = cyber_attacks_data['Device/OS'].value_counts()
+vulnerable_devices_os_df = pd.DataFrame(vulnerable_devices_os_counts).reset_index()
+vulnerable_devices_os_df.columns = ['Device/OS', 'Count of Cyber Attacks']
+
+top_vulnerable_devices_os = vulnerable_devices_os_df.head(10)
+print(top_vulnerable_devices_os)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x='Count of Cyber Attacks', y='Device/OS', hue='Count of Cyber Attacks', data=top_vulnerable_devices_os, palette='viridis', dodge=False)
+plt.xlabel('Count of Cyber Attacks')
+plt.ylabel('Device/OS')
+plt.title('Top Devices/OS Vulnerable to Cyber Attacks')
+plt.tight_layout()
+plt.show