Framework for Botnet Detection and DDoS Attack Mitigation in IoT: Synergistic Defense with Ensemble Machine Learning and Game Theory

This article introduces an end-to-end IoT cybersecurity framework that combines random forest + CNN ensemble learning, cyclic bat optimization algorithm, and game theory model to achieve botnet detection and dynamic DDoS attack mitigation, with a detection accuracy of 97.86%.

Original Author/Maintainer: ruchithra2005 Source Platform: GitHub Original Link: https://github.com/ruchithra2005/Detection-and-Mitigation-of-Botnet-DDoS-attack Release Time: June 2026

With the explosive growth of IoT devices, the network attack surface has expanded dramatically. Botnets using infected IoT devices to launch DDoS attacks have become a serious threat. Traditional rule-based and signature-based detection methods struggle to cope with rapidly evolving attack patterns, and single machine learning models have difficulty balancing detection accuracy and real-time performance.

The framework adopts a three-layer defense system:

1. Intelligent Detection Engine: Integrates random forest (processing structured features) and CNN (extracting deep spatial features), with a detection accuracy of 97.86%;
2. Sequence Optimization Algorithm: Cyclic bat optimization algorithm combined with RNN to track temporal change patterns of traffic;
3. Game Theory Decision Model: Abstracts both attack and defense parties as game participants, balancing accuracy and resource consumption when setting the protection threshold to 0.3.

• Data Processing: Uses SMOTE technology to solve class imbalance issues, extracts multi-dimensional features such as packet size distribution and time interval patterns;
• Model Training: Code modules are separated (botnet_detection.py handles basic training, ddos_mitigation.py implements optimization and game theory decisions), including 9 performance evaluation charts;
• Tech Stack: Based on Python ecosystem, relying on PyTorch/TensorFlow, Scikit-Learn, XGBoost, etc.

• IoT Security Significance: Adapts to the characteristics of IoT devices such as limited resources and diverse protocols, dynamically adjusts defense strategies;
• Academic Value: Demonstrates the potential of interdisciplinary integration of machine learning, optimization algorithms, and game theory;
• Industrial Value: Modular design is easy to integrate into existing security infrastructure, supporting progressive deployment.

Limitations: The current framework is trained on known attacks, and the generalization ability for zero-day attacks needs to be verified; real-time performance in ultra-large-scale IoT networks needs testing. Future Directions: Introduce federated learning to achieve collaborative training under privacy protection; explore graph neural networks to capture the correlation between devices.

This project proves the limitations of a single technology, and multi-technology collaboration (ensemble learning + optimization algorithm + game theory) is an effective path to address complex security threats. The 97.86% detection accuracy verifies the effectiveness of the solution, providing a practical reference for security practitioners and showing the interdisciplinary application prospects for researchers.