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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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AI-Assisted Prediction of Malware Mutation Patterns Using Diffusion Models for Proactive Cyber Defense

Arangarajan M
Assistant professor, Department of Mechanical Engineering, Shri Angalamman College of Engineering and Technology, India.
S K Rajesh Kanna
Department of Mechanical Engineering, Rajalakshmi Institute of Technology, India.
Namrata Tripathi
Assistant Professor, Department of Mathematics, Govt.College Phanda , India.

Keywords: Malware Mutation Prediction; Diffusion Models; Zero-Day Detection; Proactive Cyber Defense; AI-Based Threat Forecasting; Malware Evolution Modeling; Multi-Modal Embeddings; Cybersecurity.

Abstract

Malware is being developed at very fast rates with features such as polymorphism and mutation thus becoming ineffective in detecting these new and unknown malwares. In order to overcome this problem, this paper suggests the AI-assisted framework, predicting the mutations of malware in the future with the help of diffusion models. The system integrates the aspects of multi-modal malware, guided diffusion-based mutation generation, and temporal evolution modeling to predict how malware families are most likely to evolve with time. The identified malware detectors are then strengthened with the help of predicted mutants and enhanced to detect zero-day attack. The experimental findings demonstrate that the offered methodology will reach a high level of mutation prediction, improve the zero-day detection rate by a considerable margin, and yield tips regarding timely information about cyber attackers to defend against them proactively. This article has shown that diffusion models can be effectively utilized to receive and avert new malware threats before they happen.
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Published
2025-09-26
Pages
1-11
Issue
Vol. 4 No. 3 (2025): IJRTTE - 04 - 03
Section
Articles

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