Stream Graph
Cybersecurity Threats Stream
Stream visualization of cybersecurity threat types over time with matrix-style neon colors.
Output
Python
import matplotlib.pyplot as plt
import numpy as np
COLORS = {
'background': '#0a0a0f',
'text': '#ffffff',
'grid': '#333333',
}
np.random.seed(1414)
months = np.arange(0, 24)
# Threat types
malware = 30 + 0.8 * months + 5 * np.sin(months * np.pi / 6) + np.random.normal(0, 3, 24)
phishing = 25 + 1.2 * months + np.random.normal(0, 3, 24)
ransomware = 10 + 1.5 * months + np.random.exponential(3, 24)
ddos = 15 + 0.5 * months + 8 * np.random.random(24)
zero_day = 5 + 0.3 * months + np.random.exponential(2, 24)
data = [np.clip(d, 1, None) for d in [malware, phishing, ransomware, ddos, zero_day]]
threat_colors = ['#6CF527', '#27F5B0', '#F5276C', '#F5B027', '#27D3F5']
fig, ax = plt.subplots(figsize=(14, 6), facecolor=COLORS['background'])
ax.set_facecolor(COLORS['background'])
ax.stackplot(months, *data, colors=threat_colors, alpha=0.85, baseline='sym',
labels=['Malware', 'Phishing', 'Ransomware', 'DDoS', 'Zero-Day'])
ax.axhline(0, color=COLORS['grid'], linewidth=0.5, alpha=0.5)
ax.set_xlim(0, 23)
ax.set_title('Cybersecurity Threats Evolution', color=COLORS['text'], fontsize=14, fontweight='bold', pad=15)
ax.set_xlabel('Month', color=COLORS['text'], fontsize=11)
ax.set_ylabel('Incidents (thousands)', color=COLORS['text'], fontsize=11)
# Legend below plot
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), frameon=False,
labelcolor=COLORS['text'], fontsize=9, ncol=5)
for spine in ax.spines.values():
spine.set_visible(False)
ax.tick_params(colors=COLORS['text'], labelsize=9)
plt.tight_layout()
plt.subplots_adjust(bottom=0.18)
plt.show()
Library
Matplotlib
Category
Time Series
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