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A new prompt engineering-assisted Dynamic Malware Analysis model has been introduced, leveraging GPT-4 and achieving a 100% recall rate. This method is said to overcome certain flaws that traditional quality API call sequences may encounter during dynamic malware analysis, notably enhancing its detection performance beyond the reach of the established TextCNN approach.
- It uses GPT-4 for dynamic malware analysis, and utilizes Bidirectional Encoder Representations from Transformers (BERT) to obtain the text representation.
- It produces explanation texts for API calls, enhancing GPT-4’s ability to yield high-quality explanatory copy.
- Following the generation of these texts, BERT acts to create representations of them, which are then compiled to display the full API sequence.
- A new Convolutional Neural Network (CNN) is subsequently used to distil features from these representations for automated learning.
- The model is then linked with various malware code classes for further scrutiny.
The innovative method features two distinct tasks – Representation Generation- creating a vocabulary to produce an explanatory text for each API call, and Representation Learning – performing depthwise convolution. Each embedded channel is paired with a representation matrix, each possessing a contextual relationship with the neighboring elements. This trained module can boost the adjustment of the natural text representation for enhanced reflection.
Significantly, this experimental model was evaluated using five benchmark datasets, which were divided into two categories according to the corresponding API vocabulary. A detailed report documenting the model’s creation, the research experiments behind it, representation generation and learning, and a graph of the suggested models has been published.
This revolutionary approach to Dynamic Malware Analysis could potentially present a significant leap forward in cybersecurity, providing enhanced capacity for detection and analysis in a field increasingly reliant on effective machine learning protocols.
