Interactive Malware Analysis using RoBERTa based Model

Abstract

The rapid growth and increasing sophistication of malware pose significant threats to modern cybersecurity systems, where traditional signature-based and static analysis techniques often fail to detect evolving and zero-day attacks. This study proposes an interactive malware analysis framework leveraging a RoBERTabased SecureBERT model to perform accurate and real-time classification of malware-related text. Diverse benchmark datasets are collected and transformed into textual representations, followed by data preprocessing, finetuning, hyperparameter tuning, data balancing, and augmentation strategies to address class imbalance and improve generalization. Additionally, synthetic data generation is incorporated to enhance the detection of rare and emerging malware patterns. The SecureBERT model is fine-tuned using Low-Rank Adaptation (LoRA), enabling efficient training with reduced computational overhead while maintaining high performance. The system integrates an interactive interface that allows real-time user input and classification, improving practical usability. Experimental results demonstrate a strong performance, achieving overall accuracy of 95.33% with high precision, recall, and F1-scores across multiple malware categories. Evaluation through confusion matrices, ROC curves, and precision-recall analysis further validates robustness of the approach. Despite its effectiveness, the model shows limitations in handling highly obfuscated real-world malware due to its reliance on textual features. The proposed framework offers a scalable, adaptive, and efficient solution for malware classification, advancing intelligent cybersecurity system.