# Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis

arXiv: https://arxiv.org/abs/2312.00006 · HTML: https://mlsanity.com/paper/2312.00006/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Built on by (most cited first, 4 of 4)

- [Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling](https://mlsanity.com/paper/2401.03116/) (2024)
- [Bitcoin Price Prediction using Machine Learning and Combinatorial Fusion Analysis](https://mlsanity.com/paper/2602.00037/) (2026)
- [Enhancing SDG-Text Classification with Combinatorial Fusion Analysis and Generative AI](https://mlsanity.com/paper/2602.11168/) (2026)
- [Redefining DDoS Attack Detection Using A Dual-Space Prototypical Network-Based Approach](https://mlsanity.com/paper/2406.02632/) (2024)

## Abstract (arXiv)

Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability. While machine learning (ML) models are used for DoS attack detection, new strategies are needed to enhance their performance. We suggest an innovative method, combinatorial fusion, which combines multiple ML models using advanced algorithms. This includes score and rank combinations, weighted techniques, and diversity strength of scoring systems. Through rigorous evaluations, we demonstrate the effectiveness of this fusion approach, considering metrics like precision, recall, and F1-score. We address the challenge of low-profiled attack classification by fusing models to create a comprehensive solution. Our findings emphasize the potential of this approach to improve DoS attack detection and contribute to stronger defense mechanisms.