Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4

Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4
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Original Paper
Dynamic analysis methods effectively identify shelled, wrapped, or obfuscated malware, thereby preventing them from invading computers. As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of consecutive API calls, has progressively become the dominant feature of dynamic analysis methods. Though there have been numerous deep learning models for malware detection based on API sequences, the quality of API call representations produced by those models is limited. These models cannot generate representations for unknown API calls, which weakens both the detection performance and the generalization. Further, the concept drift phenomenon of API calls is prominent. To tackle these issues, we introduce a prompt engineering-assisted malware dynamic analysis using GPT-4. In this method, GPT-4 is employed to create explanatory text for each API call within the API sequence. Afterward, the pre-trained language model BERT is used to obtain the representation of the text, from which we derive the representation of the API sequence. Theoretically, this proposed method is capable of generating representations for all API calls, excluding the necessity for dataset training during the generation process. Utilizing the representation, a CNN-based detection model is designed to extract the feature. We adopt five benchmark datasets to validate the performance of the proposed model. The experimental results reveal that the proposed detection algorithm performs better than the state-of-the-art method (TextCNN). Specifically, in cross-database experiments and few-shot learning experiments, the proposed model achieves excellent detection performance and almost a 100% recall rate for malware, verifying its superior generalization performance. The code is available at:

Summary Notes

Enhancing Malware Detection with GPT-4: A New Frontier in AI

The battle against malware is intensifying, with traditional analysis methods struggling to keep pace.
This post explores a cutting-edge approach utilizing GPT-4, marking a significant improvement in detecting malware threats.

The Rising Complexity of Malware

As malware becomes increasingly complex, the effectiveness of traditional detection methods is diminishing. Issues such as handling novel API calls and poor generalization are driving the search for more advanced solutions.

Understanding Malware through API Sequences

API sequences are vital for decoding malware actions, but analyzing them has been challenging with conventional models due to limited training data and issues with generalization.

Introducing GPT-4 into Malware Detection

A new method using GPT-4, known as prompt engineering, includes:
  • Generating Descriptions for API Calls: Using GPT-4 to describe each API call, eliminating the need for pre-defined training datasets.
  • Embedding API Calls with Pre-trained Models: Enhancing API call descriptions using models like BERT to create detailed representations.
  • Classifying with a CNN Model: These detailed representations are then classified by a CNN model to determine if they're malicious or benign, offering a deeper insight into malware behavior.
  • Enhancing Adaptability: This method improves adaptability to new malware and API calls, addressing major issues like generalization and data drift.

Proven Success

This GPT-4 based approach has shown notable success in:
  • Enhanced Detection Performance: It outperforms current methods in accuracy and recall on benchmark datasets.
  • Handling New Malware Types: Its ability to adapt to new malware showcases its robustness.
  • Effective Across Different Conditions: The method proves effective in cross-database and few-shot learning scenarios, highlighting its versatility.

Conclusion: A New Era in Cybersecurity

Incorporating GPT-4 into malware detection signifies a substantial advancement in cybersecurity. By surpassing traditional methods' limitations, it offers a more effective, adaptable way to combat malware.
This development not only enhances current defenses but also sets the stage for future innovations in AI-based cybersecurity strategies.
For AI engineers focused on cybersecurity, adopting GPT-4 for malware detection represents a significant step forward in protecting digital infrastructures against sophisticated threats.
The evolution of AI techniques like this is crucial in our ongoing fight against cybercrime.

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