A Comprehensive Review of Natural Language Processing Techniques for Malware Detection
Nachaat AbdElatif Mohamed · 2024
In an era marked by the unprecedented proliferation of cybersecurity threats, conventional malware detection paradigms characterized by signature-based and heuristic methodologies are encountering limitations in their ability to address zero-day and polymorphic malware variants. This lacuna in the cybersecurity landscape necessitates the exploration of innovative, efficacious approaches. Natural Language Processing (NLP) has emerged as a formidable technique in this arena, exhibiting a marked capability for enhancing malware detection frameworks. The current manuscript offers an exhaustive review of NLP methodologies applied to the domain of malware detection, substantiated by a rigorous categorization of extant literature. Empirical evidence suggests that NLP-driven techniques have realized detection accuracy rates upwards of 98.7%, thereby outclassing traditional methods and underscoring the efficacy of linguistic analysis in cybersecurity applications. This review delineates the multitude of NLP techniques such as tokenization, Named Entity Recognition (NER), and sentiment analysis, whilst evaluating their operational practicability, computational efficiency, and scalability. Furthermore, the manuscript elucidates existing limitations and delineates future research vectors, notably emphasizing the requisite for real-time analytics and adaptability to a dynamically evolving threat landscape. Intended to serve as a seminal reference point, this review aims to synthesize current knowledge and foster interdisciplinary dialogue between the NLP and cybersecurity research communities.