From Packets to Predictions

A comprehensive, hands-on curriculum designed to bridge the gap between deep expertise in fixed access networking and the transformative power of modern AI

This repository serves as a comprehensive, hands-on engineering manual for applying Machine Learning (ML) to network engineering. It contains a curated collection of 35 practical projects, each designed to solve real-world networking challenges using data-driven techniques.

This portfolio is designed for network professionals, security analysts, and data scientists looking to bridge the gap between traditional networking concepts and modern machine learning. Each project includes:

  • Detailed README with business objectives and technical implementation
  • Complete Python implementation ready for Google Colab
  • Real datasets from Kaggle and public sources
  • Step-by-step instructions with code examples
  • Success criteria and performance metrics

The projects progress from foundational concepts like traffic classification and anomaly detection to advanced topics such as Reinforcement Learning for dynamic routing, NLP for intent-based networking, and predictive maintenance for optical systems.

Project Categories

I. Network Security & Anomaly Detection (Projects 1-10)

Projects 1-10: Network Traffic Classification, Basic Anomaly Detection in Network Logs, Network Traffic Volume Forecasting, DDoS Attack Detection, Predicting Network Device Failure, Network Configuration Anomaly Detection, Intelligent Traffic Routing (RL), Malware/Botnet Detection from Flow Data, Root Cause Analysis for Network Outages, Encrypted Traffic Classification

II. Advanced Security & Threat Detection (Projects 11-20)

Projects 11-20: Network-based Ransomware Detection, DNS Tunneling Detection, Identifying Lateral Movement in Networks, Phishing & Malicious URL Detection, Vulnerability Prediction in Network Devices, Network Honeypot Log Analysis, Wi-Fi Anomaly Detection, Predicting Wi-Fi Roaming Events, IoT Device Fingerprinting, RF Jamming Detection

III. Wireless & IoT Networks (Projects 21-26)

Projects 21-26: Indoor Localization using Wi-Fi RSSI, Optimizing LoRaWAN Data Rate (RL), Predicting Latency Jitter, Quality of Experience (QoE) Prediction, Automated Network Ticket Classification, BGP Anomaly Detection

IV. Cloud & Modern Networking (Projects 27-35)

Projects 27-35: Network Device Configuration Generation (NLP), Predicting Optimal MTU Size, Optical Network Fault Prediction, Virtual Network Function (VNF) Performance Prediction, Predicting Cloud Network Egress Costs, Container Network Traffic Pattern Analysis, Service Chain Placement in NFV, Detecting Noisy Neighbors in Multi-tenant Cloud, Anomaly Detection in Cloud Load Balancer Logs

The 35 Projects: A Quick Overview

# Project Title Core Concept Learned
1 Network Traffic Classification Multi-class Classification & Traffic Analysis
2 Anomaly Detection in Network Logs Unsupervised Learning & Outlier Detection
3 Network Traffic Volume Forecasting Time-Series Analysis & ARIMA Modeling
4 DDoS Attack Detection Binary Classification & Security Analytics
5 Predicting Network Device Failure Classification & Handling Imbalanced Data
6 Network Configuration Anomaly Detection Configuration Compliance & Classification
7 Intelligent Traffic Routing with RL Reinforcement Learning & Network Optimization
8 Malware & Botnet Detection from Flow Data Security Classification & Flow Analysis
9 Root Cause Analysis for Network Outages NLP & Graph ML for Troubleshooting
10 Encrypted Traffic Classification Deep Packet Inspection & Classification
11 Network-based Ransomware Detection Security Analytics & Behavioral Detection
12 DNS Tunneling Detection DNS Analysis & Security Classification
13 Lateral Movement Detection Network Security & Movement Analysis
14 Phishing & Malicious URL Detection URL Analysis & Security Classification
15 Vulnerability Prediction in Network Devices Security Assessment & Interpretable Models
16 Network Honeypot Log Analysis Clustering & Attacker Behavior Analysis
17 Wi-Fi Anomaly Detection Wireless Security & Anomaly Detection
18 Wi-Fi Roaming Prediction Mobile Network Prediction & Classification
19 IoT Device Fingerprinting Device Classification & Network Analysis
20 RF Jamming Detection Wireless Security & Signal Analysis
21 Indoor Localization using Wi-Fi RSSI Location Prediction & Signal Processing
22 LoRaWAN Data Rate Optimization Reinforcement Learning & IoT Optimization
23 Latency Jitter Prediction Network Performance & Regression Analysis
24 QoE Prediction for Video Streaming Quality Assessment & Performance Prediction
25 Automated Network Ticket Classification NLP & Text Classification for Operations
26 BGP Anomaly Detection Routing Security & Anomaly Detection
27 Network Device Configuration Generation Natural Language Processing & Config Automation
28 Optimal MTU Size Prediction Network Optimization & Regression Analysis
29 Optical Network Fault Prediction Infrastructure Monitoring & Fault Detection
30 VNF Performance Prediction NFV Analytics & Performance Forecasting
31 Cloud Network Egress Cost Prediction Cost Forecasting & Time-Series Analysis
32 Container Network Traffic Analysis Containerization & Traffic Classification
33 Service Chain Placement Optimization NFV Optimization & Reinforcement Learning
34 Noisy Neighbors Detection in Cloud Multi-tenant Analytics & Anomaly Detection
35 Anomaly Detection in Cloud Load Balancer Load Balancer Analytics & Anomaly Detection

Development Environments

Explore popular development environments for data science and machine learning

JupyterLab, Google Colab, and Kaggle are all popular platforms for learning and experimenting with machine learning and AI, but they have distinct differences in interface, capabilities, hardware access, and target use cases.

JupyterLab

  • JupyterLab is an open-source, versatile, locally-installed development environment for interactive computing.
  • It supports multiple documents (notebooks, terminals, file editors) with a flexible layout, and is ideal for advanced workflows and custom extensions.
  • Users need to install and configure it locally or on a server; hardware limitations depend on the user's machine.
  • Most suitable for those seeking full control and customization, especially for complex or multi-file projects.

Google Colab

  • Google Colab is a cloud-based, free Jupyter notebook environment provided by Google.
  • Requires no setup—just a browser and Google account—with seamless integration to Google Drive for sharing and storing notebooks.
  • Offers free access to GPUs and TPUs for faster model training; paid tiers unlock more powerful hardware and longer session durations.
  • Best for beginners, educators, and quick prototyping, especially when local hardware is insufficient for deep learning.

Kaggle

  • Kaggle is a cloud-based platform and community for data science and machine learning, operated by Google.
  • Provides kernels (Jupyter notebooks), free access to powerful GPUs (Tesla T4, P100), and a rich repository of datasets and competitions.
  • Focused on collaborative learning through competitions and shared code; optimized for data preprocessing and batch processing workflows.
  • Especially useful for those who want to learn through real-world challenges, access community-outlined solutions, and use diverse public datasets.

Key Differences Table

Platform Interface & Setup Hardware Access Collaboration Use Case Focus Free Tier Features
JupyterLab Local/multi-document User's machine Manual Custom, advanced workflows Full local access, no cloud GPUs
Google Colab Cloud browser-based Free GPU/TPU Real-time Fast prototyping Cloud storage, basic GPU, easy sharing
Kaggle Cloud notebook Free GPU/TPU Competitions/community Datasets, competitions Data repository, long sessions, GPU access

Use Colab for quick cloud-based experiments, Kaggle for learning through competitions and dataset-rich environment, and JupyterLab for customizable local development and advanced machine learning projects.

Further Reading & References