All classes Hybrid ML Network Intrusion Detection Syllabus · Episode 5 of 6

Class Hybrid ML Intrusion Detection Network IoT #5: Hybrid: Autoencoder (Anomaly) + XGBoost (Attack Classification)

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Tutorial hybrid intrusion detection system using Autoencoder and XGBoost for zero-day attacks with Python. Episode 5 of the hybrid IoT network intrusion detection machine learning series.

Software: Python (pandas, scikit-learn, XGBoost, PyTorch, Matplotlib)
Level: Advanced
Duration: ± 8 min
Series total: 52 min · 6 videos
Published: October 25, 2026

Episode 5 of 6 · view syllabus

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Introduction

Here is the final result, a hybrid system combining XGBoost and autoencoder. The detection rate increases from 66 to 87%, and detected Zero-Di attacks increase from 37 to 80%. Let's get started. Hello, welcome to episode five of the IoT network intrusion detection series with hybrid machine learning. XG,Boost is great at recognizing attacks it has seen before and rarely gives false alarms, while autoencoder can detect new attacks. Today, we are combining both into a two-layer system, an architecture widely used in modern I-DS research.

Step 1: Two-layer hybrid system

Create a file named hybrid.py We load the XGBoost and autoencoder models from the previous episode, then calculate the predicted class of XGBoost and the anomaly score for each test connection. We also mark Zero-Di attacks, which are test connections whose attack types were never present in the training data. The hybrid function works in two layers. If XGBoost labels an attack, that category is used. If XGBoost labels normal but the autoencoder error exceeds the threshold, the connection is labeled as a new anomaly. Then we compare four configurations. Run XGBoost alone detects 65.6% of attacks and only 37% of Zero-Di attacks. Autoencoder alone detects 81.6%. Hybrid with a 95th percentile threshold detects 86.7% of attacks and 80% of Zero-Di attacks, with the highest accuracy of 0.879. With a 99th percentile, false alarms drop to 7.8% but detection drops to 75%.

Step 2: Detection curve & per category

Continue with the graph We shift the threshold from the 50th percentile to 99.9% to plot the detection rate curve against false alarm rate, as well as the detection diagram per category. Let's look at the graph. The hybrid curve is always above the autoencoder alone, meaning, for the same false alarm rate, hybrid always detects more attacks, and the XGBoost star is below both curves. The right diagram shows the largest jump in U2R and R2L, categories that contain the most new attacks. The threshold selection is an operational decision, a hospital network might choose high detection, while a busy data center might choose low false alarm rate.

Done!

Done. We have built a hybrid system of autoencoder and XGBoost, analyzed the trade-off between detection and false alarm, and proved its superiority against zero-day attacks. In the final episode, we will evaluate it comprehensively and create a monitoring dashboard. Currently working on a thesis, dissertation, or research in the field of network security and machine learning? Cerdas Inovasi Digital helps implement intrusion detection systems from network traffic preprocessing, feature selection, Random Forest and XGBoost, up to autoencoder for Zero Day attacks and monitoring dashboard, as well as opening Python programming courses for electrical engineering and computer science. Want to master the entire series? From intrusion datasets to hybrid systems ready for monitoring? All six episodes, complete with step-by-step PDF guides, dataset, and source code, are available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or one-time payment of Rp999.000 for lifetime access. Want just one class? Buy the single class with lifetime access. Subscribe now on the premium page cerdasinovasidigital.com. Contact our WhatsApp, or visit cerdasinovasidigital.com. Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

0:44Step 1: Two-layer hybrid system

4:26Step 2: Detection curve & per category

6:39Done!

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Transcript

0:03Introduction

0:44Step 1: Two-layer hybrid system

4:26Step 2: Detection curve & per category

6:39Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Two-layer hybrid system

Step 2: Detection curve & per category

Done!

Introduction

Step 1: Two-layer hybrid system

Step 2: Detection curve & per category

Done!