Cyber Threats Analysis Bubble

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)

fig, ax = plt.subplots(figsize=(14, 9), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

threats = ['Ransomware', 'Phishing', 'DDoS', 'Data Breach', 'Supply Chain', 'Zero-Day', 'Insider', 'APT']
severity = np.array([9.5, 6, 7, 9, 8.5, 9.8, 7.5, 9.2])
frequency = np.array([620, 3400, 850, 290, 45, 15, 180, 25])
avg_cost = np.array([4.5, 0.5, 0.8, 4.2, 8.5, 12, 2.5, 15])

colors = ['#C82909', '#F5B027', '#27D3F5', '#F5276C', '#4927F5', '#9C2007', '#6CF527', '#F54927']
sizes = avg_cost * 100

for glow_mult, glow_alpha in [(3.5, 0.02), (2.8, 0.04), (2.2, 0.06), (1.7, 0.10), (1.3, 0.15)]:
    ax.scatter(severity, frequency, s=sizes*glow_mult, c=colors, alpha=glow_alpha, edgecolors='none')

ax.scatter(severity, frequency, s=sizes, c=colors, alpha=0.9, edgecolors='none')
ax.scatter(severity, frequency, s=sizes*0.4, c=colors, alpha=0.4, edgecolors='none')
ax.scatter(severity - np.sqrt(sizes)*0.008, frequency + np.sqrt(sizes)*2, s=sizes*0.15, c='white', alpha=0.5, edgecolors='none')

for i, threat in enumerate(threats):
    offset_y = np.sqrt(sizes[i])/2 + 10
    ax.annotate(threat, (severity[i], frequency[i]), fontsize=10, color='white',
                ha='center', va='bottom', xytext=(0, offset_y), textcoords='offset points', fontweight='bold')

ax.text(0.0, 1.08, 'Cyber Threats Analysis', transform=ax.transAxes, fontsize=24, color='white', fontweight='bold')
ax.text(0.0, 1.02, 'Severity vs Frequency · Bubble size = Avg Cost', transform=ax.transAxes, fontsize=11, color='#555555')

ax.set_xlabel('Severity Score (1-10)', fontsize=14, color='#888888', fontweight='500', labelpad=15)
ax.set_ylabel('Annual Incidents (Thousands)', fontsize=14, color='#888888', fontweight='500', labelpad=15)
ax.tick_params(colors='#555555', labelsize=11, length=0)
ax.set_xlim(5, 10.5)

for y in [0, 1000, 2000, 3000, 4000]:
    ax.axhline(y=y, color='#1a1a2e', linewidth=0.8, zorder=0)
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()
plt.show()