TAD-LLM: API Traffic Anomaly Detection Based on Large Language Model

APIs are increasingly prevalent in application environments, carrying the core business logic and sensitive data of enterprises, and have increasingly become the target of cyber attackers. The proportion of web attacks targeting APIs has exceeded half. The widespread use of APIs has expanded the attack surface, posing serious security challenges. Security risks, such as unauthorized access, misuse of business logic, data breaches, and complex cyber attacks, have intensified. Tr aditional security measures have proven inadequate in addressing API threats. There is an urgent demand for a more contextually aware and intelligent security mechanism capable of effectively mitigating API attacks. We proposed a novel model TAD-LLM based on Large Language Model for anomaly detection in API traffic. By using S2GS data transformation method, prompt optimization algorithm and LoRA fine-tuning technique, enables the model to acquire a profound comprehension of domain-specific knowledge in more elaborate detail, thereby enhancing the overall detection capability. Experimental results demonstrate that the proposed model TAD-LLM makes a significant advancement in securing APIs against cyber threats. The average f1-score of TAD-LLM reaches 99.27% in complex API attack scenarios. There are also notable improvements in precision, recall, and accuracy. Moreover, the overall performance of the model indicates that the model we proposed outperforms other models significantly and exhibits superior capability in handling complex API attack scenarios and advanced API attack techniques. It is worth noting that our model also shows strong performance on CSIC 2010, a widely used common http traffic dataset.