Unsupervised Learning Intermediate

Unsupervised learning discovers hidden structures in data without labeled examples. In networking, this is invaluable because most network data is unlabeled - you cannot manually tag every packet or event as normal or anomalous.

Clustering for Network Analysis

Clustering groups similar data points together. In networking, clustering helps identify:

• Device Groups - Automatically discover groups of devices with similar behavior patterns
• Traffic Profiles - Identify distinct traffic patterns (business hours web, overnight backups, video streaming)
• User Segments - Group users by their network usage patterns for QoS policy design

K-Means Clustering Example

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Network device metrics: CPU, memory, interface utilization, error rate
features = df[['cpu_pct', 'mem_pct', 'if_util', 'error_rate']]
scaler = StandardScaler()
scaled = scaler.fit_transform(features)

# Find 4 device behavior clusters
kmeans = KMeans(n_clusters=4, random_state=42)
df['cluster'] = kmeans.fit_predict(scaled)

# Interpret clusters: "idle", "normal", "busy", "stressed"
for c in range(4):
    print(f"Cluster {c}: {len(df[df.cluster==c])} devices")
    print(df[df.cluster==c][features.columns].mean())

Anomaly Detection with Autoencoders

Autoencoders learn to compress and reconstruct normal network data. When an anomalous event occurs, the reconstruction error is high, flagging it as unusual.

Dimensionality Reduction with PCA

Networks generate dozens of metrics per device. PCA reduces this to a smaller set of principal components while preserving the most important variation, making visualization and further analysis practical.

Algorithm Comparison