Interactive Malware Analysis using RoBERTa

Abstract

The rapid surge of malware presents significant threats to device security and user privacy, with traditional detection methods often struggling to keep pace with its evolving sophistication and variety. The escalating complexity and diversification of malware create a pressing need for advanced detection techniques that can effectively identify and mitigate these emerging cyber threats. This study addresses these challenges by employing advanced transformer-encoder models to create rich contextual representations from diverse malware datasets, aiming to significantly enhance
the accuracy and performance of malware detection systems. To this end, relevant malware datasets are systematically collected, converted into text format and contextualized according to various malware labels to ensure a comprehensive analysis. The preprocessed data is then analyzed using the SecureBERT model, which is built upon the robust RoBERTa architecture, facilitates precise classification of malware types. The model’s effectiveness is validated through interactive prompts, successfully categorizing input text messages into corresponding malware categories. Experimental results demonstrate a significant improvement in classification performance, highlighting the model’s capability to analyze real-time texts related to malware effectively. This innovative approach offers substantial potential for enhancing cybersecurity measures and represents a breakthrough in the field of malware detection and analysis, paving the way for more effective strategies against emerging cyber threats.