# AI-Based-Honeypot-Attack-Detection-System: A Network Attack Detection System Based on Honeypots and Machine Learning

> A network attack detection system that combines Cowrie and Dionaea honeypot technologies with the Random Forest machine learning algorithm. It can extract features from honeypot logs and automatically classify brute-force attacks and interactive attacks.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-16T16:45:51.000Z
- 最近活动: 2026-06-16T16:53:26.389Z
- 热度: 154.9
- 关键词: 蜜罐, 网络安全, 机器学习, 随机森林, Cowrie, Dionaea, 攻击检测, 威胁情报, scikit-learn, 特征工程
- 页面链接: https://www.zingnex.cn/en/forum/thread/ai-based-honeypot-attack-detection-system
- Canonical: https://www.zingnex.cn/forum/thread/ai-based-honeypot-attack-detection-system
- Markdown 来源: floors_fallback

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## [Introduction] Core Overview of AI-Based-Honeypot-Attack-Detection-System

Project Name: AI-Based-Honeypot-Attack-Detection-System
Original Author: Sidd1007
Source: GitHub (Link: https://github.com/Sidd1007/AI-Based-Honeypot-Attack-Detection-System)
Core Functions: Combines Cowrie and Dionaea honeypot technologies with the Random Forest machine learning algorithm, extracts features from honeypot logs and automatically classifies brute-force attacks and interactive attacks, providing intelligent analysis capabilities for network security defense.

## [Background] Basics of Honeypot Technology and Types Used in the Project

### Definition of Honeypot
A security mechanism that lures attackers by setting up seemingly valuable targets, records and analyzes attack behaviors, and any access can be regarded as suspicious activity.
### Honeypots Used in the Project
1. **Cowrie**: A medium-interactive SSH/Telnet honeypot that simulates a Unix environment, recording brute-force attacks, shell interactions, command execution, file downloads, etc.
2. **Dionaea**: A low-interactive honeypot that simulates vulnerable services like SMB and MSSQL, capturing malware payloads.

## [Methodology] System Workflow

System operation is divided into 6 phases:
1. **Honeypot Deployment**: Deploy Cowrie and Dionaea in an isolated environment, configure logs and monitoring.
2. **Attack Capture**: Record incoming connections, login attempts, command execution, file downloads, etc.
3. **Feature Extraction**: Extract features from logs (total number of attempts, number of unique usernames, number of failed/successful attempts, average time interval, number of command executions, session duration).
4. **Dataset Generation**: Organize features into CSV format.
5. **Model Training**: Use scikit-learn to train a Random Forest classifier (ensemble learning to improve accuracy and robustness).
6. **Attack Prediction**: The model classifies new activities as brute-force attacks or interactive attacks.

## [Technical Details] Feature Engineering and Model Performance

### Feature Engineering
Feature design reflects attack patterns:
- High-frequency login attempts with short intervals → Brute-force attack
- Complex command sequences + long sessions → Interactive attack
- Large number of unique usernames → Dictionary attack
### Model Performance
The Random Forest classifier achieves an average cross-validation accuracy of 71.67% (considering attack diversity and log noise, this is a usable benchmark).
### Visualization
Provides scripts for confusion matrix and feature importance visualization to help understand the model's decision logic.

## [Application Scenarios] Project Value and Use Cases

1. **Security Operations Center (SOC)**: Automatically classify honeypot alerts, helping analysts prioritize high-risk interactive attacks.
2. **Threat Intelligence Collection**: Accumulate labeled data to train more accurate models, collect attacker behavior patterns and tool preferences.
3. **Security Research and Education**: Provide practical cases for students/beginners, covering honeypot deployment, log analysis, feature engineering, and machine learning applications.

## [Improvement Directions] Project Optimization Suggestions

1. **Expand Honeypot Types**: Introduce Conpot (industrial control systems), Glastopf (web applications), etc., to expand attack coverage.
2. **Try Other Algorithms**: Such as XGBoost, LightGBM, or deep learning models to improve accuracy.
3. **Real-Time Detection**: Expand from offline batch processing to a real-time stream processing architecture.
4. **Attacker Profiling**: Combine multi-honeypot data to build behavior profiles and correlation analysis.

## [Summary] Project Significance and Target Audience

AI-Based-Honeypot-Attack-Detection-System is a typical project combining traditional security technology with modern AI, demonstrating the process of converting honeypot data into machine learning features and the application of Random Forest.
Target Audience:
- Students who are new to network security machine learning
- Security engineers who need to quickly build an attack detection prototype
It provides a clear and runnable starting point for relevant personnel.