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CPU Anomaly Detection System — ML-2

Overview

Real-time CPU monitoring system that detects anomalies using Statistical and ML methods.

Dataset

  • cpu_utilization_asg_misconfiguration.csv
  • 18,050 readings | May 2014 → July 2014

Methods Used

  • Statistical Detection (Mean + 2σ threshold)
  • Isolation Forest (Scikit-learn)
  • PyOD Anomaly Detection

Results

  • Total Anomalies: 1,450 (8.03%)
  • Critical: 826 | Warning: 624

Tech Stack

Python, Pandas, NumPy, Scikit-learn, PyOD, Matplotlib

How to Run

  1. Open notebook in Google Colab
  2. Upload dataset from Data/ folder
  3. Run all cells step by step
  4. Check Output/ for results