cybersecurity_attacks.py

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