# Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning

arXiv: https://arxiv.org/abs/2012.06805 · HTML: https://mlsanity.com/paper/2012.06805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security](https://mlsanity.com/paper/2503.04174/) (2025)

## Abstract (arXiv)

DDoS attacks are simple, effective, and still pose a significant threat even after more than two decades. Given the recent success in machine learning, it is interesting to investigate how we can leverage deep learning to filter out application layer attack requests. There are challenges in adopting deep learning solutions due to the ever-changing profiles, the lack of labeled data, and constraints in the online setting. Offline unsupervised learning methods can sidestep these hurdles by learning an anomaly detector N from the normal-day traffic mathcal N. However, anomaly detection does not exploit information acquired during attacks, and their performance typically is not satisfactory. In this paper, we propose two frameworks that utilize both the historic mathcal N and the mixture mathcal M traffic obtained during attacks, consisting of unlabeled requests. We also introduce a machine learning optimization problem that aims to sift out the attacks using mathcal N and mathcal M. First, our proposed approach, inspired by statistical methods, extends an unsupervised anomaly detector N to solve the problem using estimated conditional probability distributions. We adopt transfer learning to apply N on mathcal N and mathcal M separately and efficiently, combining the results to obtain an online learner. Second, we formulate a specific loss function more suited for deep learning and use iterative training to solve it in the online setting. On publicly available datasets, our online learners achieve a 99.3% improvement on false-positive rates compared to the baseline detection methods. In the offline setting, our approaches are competitive with classifiers trained on labeled data.