Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification

• URL: http://arxiv.org/abs/2601.13197v1
• Date: Mon, 19 Jan 2026 16:22:27 GMT
• Title: Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification
• Authors: Aravind B, Anirud R. S., Sai Surya Teja N, Bala Subrahmanya Sriranga Navaneeth A, Karthika R, Mohankumar N,
• Abstract summary: Class imbalance in network intrusion detection using Tabular Denoising Diffusion Probability Models (TabDDPM)<n>Our approach synthesizes high-fidelity minority-class samples from the CIC-IDS 2017 dataset through iterative denoising processes.<n>For the minority classes that have smaller samples, synthetic samples were generated and merged with the original dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class imbalance refers to a situation where certain classes in a dataset have significantly fewer samples than oth- ers, leading to biased model performance. Class imbalance in network intrusion detection using Tabular Denoising Diffusion Probability Models (TabDDPM) for data augmentation is ad- dressed in this paper. Our approach synthesizes high-fidelity minority-class samples from the CIC-IDS2017 dataset through iterative denoising processes. For the minority classes that have smaller samples, synthetic samples were generated and merged with the original dataset. The augmented training data enables an ANN classifier to achieve near-perfect recall on previously underrepresented attack classes. These results establish diffusion models as an effective solution for tabular data imbalance in security domains, with potential applications in fraud detection and medical diagnostics.

Related papers

• Learning Robust Diffusion Models from Imprecise Supervision [75.53546939251146]
  DMIS is a unified framework for training robust Conditional Diffusion Models from Imprecise Supervision.<n>Our framework is derived from likelihood and decomposes the objective into generative and classification components.<n>Experiments on diverse forms of imprecise supervision, covering tasks covering image generation, weakly supervised learning, and dataset condensation demonstrate that DMIS consistently produces high-quality and class-discriminative samples.
  arXiv  Detail & Related papers  (2025-10-03T14:00:32Z)
• Improving Predictions on Highly Unbalanced Data Using Open Source Synthetic Data Upsampling [0.0]
  We show that synthetic data can improve predictive accuracy for minority groups by generating diverse data points that fill gaps in sparse regions of the feature space.<n>We evaluate the effectiveness of an open-source solution, the Synthetic Data SDK by MOSTLY AI, which provides a flexible and user-friendly approach to synthetic upsampling for mixed-type data.
  arXiv  Detail & Related papers  (2025-07-22T10:11:32Z)
• Less is More: Adaptive Coverage for Synthetic Training Data [20.136698279893857]
  This study introduces a novel sampling algorithm, based on the maximum coverage problem, to select a representative subset from a synthetically generated dataset.<n>Our results demonstrate that training a classifier on this contextually sampled subset achieves superior performance compared to training on the entire dataset.
  arXiv  Detail & Related papers  (2025-04-20T06:45:16Z)
• Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning [23.098551349745815]
  We propose a pipeline-based data augmentation method via large language models (LLMs)<n>We introduce the Gaussian-decayed gradient-assisted Contrastive Sentence Embedding (GCSE) model to enhance unsupervised sentence embeddings.<n> Experimental results show that our approach achieves state-of-the-art performance in semantic textual similarity tasks.
  arXiv  Detail & Related papers  (2024-09-19T16:29:58Z)
• Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering [0.5735035463793009]
  We introduce a framework to enhance the SMOTE algorithm using Variational Autoencoders (VAE) Our approach systematically quantifies the density of data points in a low-dimensional latent space using the VAE, simultaneously incorporating information on class labels and classification difficulty. Empirical studies on several imbalanced datasets represent that this simple process innovatively improves the conventional SMOTE algorithm over the deep learning models.
  arXiv  Detail & Related papers  (2024-05-30T07:06:02Z)
• SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data [9.969882349165745]
  In the field of data mining and machine learning, commonly used classification models cannot effectively learn in unbalanced data. Most of the classical oversampling methods are based on the SMOTE technique, which only focuses on the local information of the data. We propose a novel oversampling method SEMRes-DDPM.
  arXiv  Detail & Related papers  (2024-03-09T14:01:04Z)
• Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection [82.43311784594384]
  Real-world datasets contain not only noisy labels but also class imbalance. We propose a simple yet effective method to address noisy labels in imbalanced datasets.
  arXiv  Detail & Related papers  (2024-02-17T10:34:53Z)
• MCRAGE: Synthetic Healthcare Data for Fairness [3.0089659534785853]
  We propose Minority Class Rebalancing through Augmentation by Generative modeling (MCRAGE) to augment imbalanced datasets. MCRAGE involves training a Denoising Diffusion Probabilistic Model (CDDPM) capable of generating high-quality synthetic EHR samples from underrepresented classes. We use this synthetic data to augment the existing imbalanced dataset, resulting in a more balanced distribution across all classes.
  arXiv  Detail & Related papers  (2023-10-27T19:02:22Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)
• Negative Data Augmentation [127.28042046152954]
  We show that negative data augmentation samples provide information on the support of the data distribution. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
  arXiv  Detail & Related papers  (2021-02-09T20:28:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.