Generative Modeling of Networked Time-Series via Transformer Architectures

TL;DR

This paper introduces a transformer-based generative model for networked time-series data that enhances machine learning performance by producing high-quality synthetic samples, achieving state-of-the-art results across various datasets.

Contribution

The paper presents a novel, efficient transformer architecture for generating high-quality time-series data that improves ML model performance, addressing data scarcity in security applications.

Findings

Achieves state-of-the-art results in time-series generation.

Produces high-quality, generalizable synthetic data.

Boosts ML model performance with generated samples.

Abstract

Many security and network applications require having large datasets to train the machine learning models. Limited data access is a well-known problem in the security domain. Recent studies have shown the potential of Transformer models to enlarge the size of data by synthesizing new samples, but the synthesized samples don't improve the models over the real data. To address this issue, we design an efficient transformer-based model as a generative framework to generate time-series data, that can be used to boost the performance of existing and new ML workflows. Our new transformer model achieves the SOTA results. We style our model to be generalizable and work across different datasets, and produce high-quality samples.