Explainable Deep Neural Networks for Real-Time Malware Classification in Enterprise Systems

• Author(s): Dr. Deepak Tomar ; Dr. Kismat Chhillar ; Prof. Alok Verma
• Paper ID: 1710529
• Page: 496-502
• Published Date: 11-09-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

Abstract

The rise and growing complexity of malware present a serious and ongoing threat to enterprise systems. Traditional methods that rely on signatures for detection just aren't cutting it anymore when it comes to dealing with polymorphic and zero-day threats. Enter deep neural networks (DNNs), which have proven to be a robust solution, boasting high accuracy and the capability to identify new malware variants by learning intricate patterns from extensive datasets. However, their "black-box" nature—meaning we can't easily understand how they make decisions—can be a barrier to their use in critical enterprise security situations. This paper introduces a thorough framework for real-time malware classification using explainable deep neural networks (XDNNs) tailored for enterprise environments. We suggest an architecture that combines a high-performance deep learning model with post-hoc explainability techniques like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Our method analyzes both static and dynamic malware features to achieve impressive detection accuracy while also giving security analysts valuable insights into the model's decision-making process. We assess the trade-offs between model performance, computational demands, and the clarity of explanations, showing that XDNNs can strike a crucial balance between effectiveness and interpretability, ultimately fostering trust and enhancing the operational efficiency of a security operations center (SOC).

Keywords

Explainable AI (XAI), Deep Learning, Deep Neural Networks (DNNs), Malware Classification, Real-time Malware Detection, Enterprise Security, Cybersecurity.

Citations

IRE Journals:
Dr. Deepak Tomar , Dr. Kismat Chhillar , Prof. Alok Verma "Explainable Deep Neural Networks for Real-Time Malware Classification in Enterprise Systems" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 496-502

IEEE:
Dr. Deepak Tomar , Dr. Kismat Chhillar , Prof. Alok Verma "Explainable Deep Neural Networks for Real-Time Malware Classification in Enterprise Systems" Iconic Research And Engineering Journals, 9(3)