# Hands-On Practice of Machine Learning-Based Network Intrusion Detection System: Building Real-Time Security Protection with Random Forest

> This article introduces a complete machine learning-based network intrusion detection system project, covering the entire workflow from data preprocessing, feature engineering to model training and deployment, using a random forest classifier to identify network attack types such as DoS, Probe, R2L, and U2R.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-04T17:45:58.000Z
- 最近活动: 2026-06-04T17:50:33.527Z
- 热度: 159.9
- 关键词: 网络安全, 入侵检测, 机器学习, 随机森林, DoS攻击, Python, Flask, IBM Cloud
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-archanamalluri5-eng-network-intrusion-detection-system-nids-using-machine-learni
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-archanamalluri5-eng-network-intrusion-detection-system-nids-using-machine-learni
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Hands-On Practice of Machine Learning-Based Network Intrusion Detection System: Building Real-Time Security Protection with Random Forest

This article introduces a complete machine learning-based network intrusion detection system project, covering the entire workflow from data preprocessing, feature engineering to model training and deployment, using a random forest classifier to identify network attack types such as DoS, Probe, R2L, and U2R.

## Original Author and Source

- **Original Author/Maintainer**: Malluri Archana (archanamalluri5-eng)
- **Source Platform**: GitHub
- **Original Project Title**: Network-Intrusion-Detection-System-NIDS-using-Machine-Learning
- **Original Link**: https://github.com/archanamalluri5-eng/Network-Intrusion-Detection-System-NIDS-using-Machine-Learning
- **Publication Time**: June 2026

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## Project Background and Significance

In today's highly interconnected digital world, cybersecurity threats are becoming increasingly severe.

Traditional rule-based intrusion detection systems often struggle to handle new attack methods, while the introduction of machine learning technology has brought revolutionary changes to cybersecurity protection.

Network Intrusion Detection System (NIDS) is a key component for protecting network infrastructure. It continuously monitors network traffic, identifies abnormal behaviors and potential attacks, and helps organizations detect and respond to security threats in a timely manner. Unlike passive defense, NIDS can actively analyze traffic patterns and issue alerts before attacks cause substantial damage.

This project demonstrates a complete machine learning-driven NIDS implementation, using the random forest algorithm to classify network connections and accurately distinguish between normal traffic and various types of network attacks.

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## Attack Types and Classification System

The network attacks targeted by this project are mainly divided into four categories, covering the complete attack chain from external scanning to internal privilege escalation:

## 1. DoS (Denial of Service Attack)

DoS attacks aim to exhaust the target system's resources (bandwidth, computing power, memory, etc.) so that it cannot provide services to normal users. Common implementations include SYN Flood, UDP Flood, etc. This type of attack is characterized by large traffic volume and obvious features, but if not blocked in time, it may cause the service to be unavailable for a long time.

## 2. Probe (Probing Attack)

Probe attacks are reconnaissance actions by attackers before launching actual attacks, including port scanning, vulnerability detection, service identification, etc. Although Probe itself does not directly cause damage, it provides key intelligence for subsequent attacks. Timely detection of Probe behavior can intercept attacks at an early stage.

## 3. R2L (Remote to Local Attack)

R2L attacks refer to attackers attempting to gain access to local systems from remote network locations. Typical examples include brute-force cracking using weak passwords, gaining shell access using known vulnerabilities, etc. The success of this type of attack means that the attacker has breached the network boundary.

## 4. U2R (User to Root Attack)

U2R attacks occur when an attacker, after obtaining ordinary user privileges, attempts to escalate privileges to the system administrator (Root) level. Common methods include exploiting local privilege escalation vulnerabilities, configuration errors, etc. A successful attacker will have full control over the system.

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