Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.075265
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Negative-One-Day Malware Detection with Generative AI: A Stable Diffusion-Based Proactive Defense Framework

Sohail Khan1,*, Toqeer Ali Syed2, Mohammad Nauman1, Salman Jan3, It Ee Lee4, Qamar Wali4
1 Department of Computer Science, Effat College of Engineering, Effat University, Jeddah, Saudi Arabia
2 Faculty of Computer and Information System, Islamic University of Madinah, Madinah, Saudi Arabia
3 Faculty of Computer Studies, Arab Open University, A’Ali, Bahrain
4 Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya, Malaysia
* Corresponding Author: Sohail Khan. Email: sohkhan@effatuniversity.edu.sa
(This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.075265

Received 28 October 2025; Accepted 31 March 2026; Published online 17 April 2026

Abstract

The detection of zero-day malware represents one of the most significant challenges in contemporary cybersecurity. In this paper, we introduce a novel concept called “Negative-One-Day Malware Detection”, which aims to identify potentially malicious software before it is actually created by threat actors. Our approach leverages recent advancements in generative AI, specifically diffusion-based generative models, to generate and analyze potential future malware variants. By doing so, we can train detection systems to recognize these variants before they emerge in the wild, thereby closing the critical protection gap that currently exists between malware creation and detection. We demonstrate the effectiveness of our approach through extensive experimentation, showing that our framework can generate executable malware samples that combine characteristics from different families while exhibiting novel behaviors. These synthetically generated samples significantly improve the detection capabilities of security systems when incorporated into training data, providing a proactive rather than reactive approach to cybersecurity.

Keywords

Adversarial machine learning; Generative AI; stable diffusion models; zero-day malware detection; negative-one-day malware detection; proactive cyber defense

  • 195

    View

  • 31

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link