# Machine Learning-Based Network Anomaly Detection System: Building an Efficient Intrusion Detection Solution Using Random Forest

> This article introduces a network intrusion detection system (NIDS) based on the random forest classifier. The system uses two benchmark datasets, NSL-KDD and CICIDS2017, for training and evaluation, achieving a detection accuracy of up to 99.9% and providing a practical machine learning solution for network security protection.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-14T13:15:59.000Z
- 最近活动: 2026-06-14T13:19:18.367Z
- 热度: 159.9
- 关键词: 机器学习, 网络安全, 入侵检测, 随机森林, NSL-KDD, CICIDS2017, Python, 异常检测
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-adityaraikar5555-collab-network-anomaly-detection-using-machine-learning
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-adityaraikar5555-collab-network-anomaly-detection-using-machine-learning
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Machine Learning-Based Network Anomaly Detection System: Building an Efficient Intrusion Detection Solution Using Random Forest

This article introduces a network intrusion detection system (NIDS) based on the random forest classifier. The system uses two benchmark datasets, NSL-KDD and CICIDS2017, for training and evaluation, achieving a detection accuracy of up to 99.9% and providing a practical machine learning solution for network security protection.

## Original Author and Source

- **Original Author/Maintainer**: adityaraikar5555-collab
- **Source Platform**: GitHub
- **Original Title**: Network-Anomaly-Detection-Using-Machine-Learning
- **Original Link**: https://github.com/adityaraikar5555-collab/Network-Anomaly-Detection-Using-Machine-Learning
- **Publication Date**: June 14, 2026

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## Background: The Growing Severity of Cybersecurity Threats

With the acceleration of digital transformation, the frequency and complexity of cyberattacks are rising rapidly. Traditional rule-based intrusion detection systems often struggle to handle new attack methods, with high false positive rates and huge maintenance costs. Against this backdrop, machine learning-based Network Intrusion Detection Systems (NIDS) have emerged as a focus of attention in academia and industry.

Machine learning technology can automatically learn normal behavior patterns from massive network traffic data and identify abnormal traffic that deviates from these patterns. Compared to static rules, machine learning models have stronger generalization and adaptive capabilities, enabling effective detection of zero-day attacks and unknown threats.

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## Project Overview: A Detection Solution Driven by Two Datasets

This project builds a complete machine learning-driven network intrusion detection system, whose core feature is the simultaneous use of two industry-recognized benchmark datasets for model training and performance verification:

## NSL-KDD Dataset

NSL-KDD is an improved version of the KDD99 dataset, addressing the redundant records and duplication issues in the original dataset. The dataset includes the following files:
- `KDDTrain+.txt` - Training data
- `KDDTest-21.txt` - Test data

The dataset covers various types of network attacks, including Denial of Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R) attacks.

## CICIDS2017 Dataset

CICIDS2017 is a modern network intrusion detection dataset released by the Canadian Institute for Cybersecurity, containing attack types that are more relevant to current network environments:
- DDoS attack
- Port Scan
- Web attack
- Infiltration
- Brute Force
- Botnet
- Normal traffic

The advantages of this dataset lie in its more modern attack types and more realistic traffic characteristics, which can better evaluate the model's performance in real-world scenarios.

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## Technical Implementation: Selection of Random Forest Classifier

The project selected Random Forest as the core classification algorithm, based on the following key considerations:

## Why Choose Random Forest?

1. **High Accuracy**: Random Forest significantly improves classification accuracy by integrating the prediction results of multiple decision trees
2. **Handling Large-Scale Data**: Network traffic data is usually large in volume, and Random Forest can efficiently process high-dimensional features
3. **Anti-Overfitting**: Through random feature selection and Bagging technology, Random Forest has strong robustness against overfitting
4. **Feature Importance Analysis**: It can output the contribution of each feature to the classification result, helping to understand the model's decision logic
5. **Fast Prediction**: After training, the model has fast inference speed, suitable for real-time detection scenarios