This PySpark notebook performs network forensic analysis using unsupervised clustering to identify patterns and potential anomalies in network connection logs. Here’s what it does, step-by-step:

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1. Data Loading and Schema Definition

• Imports modules for PySpark SQL, data types, machine learning, vectorization, and user-defined functions.
• Defines a custom schema for reading a tab-separated network log file ("bigger.log") with fields like timestamps, IPs, ports, protocol, bytes, and packet counts.
• Reads the log file into a Spark DataFrame df and replaces all missing values with zeros (df2).

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2. Feature Engineering

• Defines a user-defined function (toInt) to convert string fields (like IPs, proto, ports, and byte counts) to integers by concatenating their ASCII values—useful for numeric machine learning features.
• Registers the UDF, then applies it across relevant columns to create integerized columns such as iorigp, irespp, iproto, iorigbytes, irespbytes, iorigpkts, and iorigipbytes.

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3. Vectorization

• Uses VectorAssembler to combine selected integer feature columns into a single features vector column for ML.
• Resulting DataFrame, router, is ready for clustering.

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4. Clustering Analysis (K-Means)

• Trains a KMeans model (k=7 clusters, fixed seed) on the feature vectors.
• Generates cluster predictions for each connection record and appends the predicted cluster to the DataFrame.

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5. Cluster Analysis and Visualization

• Groups the predictions by cluster label to create a count of records per cluster.
• Converts the groupby result to a Pandas DataFrame and plots a bar chart with Plotly, showing the distribution of connection records across clusters.
• Displays the cluster count table for deeper inspection.

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6. Anomaly Identification

• Filters the records assigned to cluster #2 (as an example "suspect" cluster).
• Selects fields such as timestamp, connection UID, original host, and responding host for further manual inspection or investigation.

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Summary

• Purpose: To cluster massive network logs and identify group-level patterns or potential anomalies for security or operational forensics.
• Workflow:
  • Loads tab-separated connection logs as a Spark DataFrame with strict schema.
  • Converts key fields to numeric for ML compatibility.
  • Performs clustering with K-Means.
  • Analyzes and visualizes the size of each cluster.
  • Filters and extracts suspicious or interesting subgroups for further analysis.
• Result: Delivers a scalable, ML-driven pipeline for exploring and labeling network behavior, highlighting potentially unusual clusters for further review.