.png){kind=icon}
.png){kind=icon}
Return
Efficient malware detection using NLP and deep learning model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.aej.2025.03.118.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Malware has emerged as a significant challenge in contemporary society, growing in tandem with technological advancements. Consequently, the classification of malware has become a pressing concern for various services. Conventional malware detection techniques, such as signature matching, are constrained by the dynamic evolution of malware, which limits their adaptability and efficacy. To tackle these issues, this study employs natural language processing (NLP) and deep learning approaches to categorize malware entities as either malicious or benign. The model incorporates image processing by transforming code segments into image pixels, applying convolutional operations, and utilizing advanced deep learning methodologies. Following processing, the model generates a normalized value through the sigmoid function, which is then rounded to yield a binary classification. The results were validated using multiple metrics, including precision and accuracy, to evaluate the model's effectiveness and ensure optimal performance throughout the classification process. The proposed model's performance was assessed on datasets of kernel API calls by the malware. The research highlights that using NLP from the function calls and deep learning techniques for malware classification enhances the accuracy and adaptability of detecting malicious software which overcomes the limitations of traditional signature-based methods. The model delivers encouraging results, presenting a viable solution for effective malware classification. This paper aims to experiment with different variables of a malicious code that are often overlooked while analysing a malware.
Keywords:
Malware
Artificial Intelligence
Natural Language Processing
Deep Learning
Classification
Journal
IF:
6.8
Papers:
6.3K
Citations:
2.6W
View more organizations