TIM: threat context-enhanced TTP intelligence mining on unstructured threat data

Cybersecurity

Huamin Feng¹  Xuren Wang²  Peian Yang³  Yizhe You⁴  Ning Li⁴  Jun Jiang⁴  Zhengwei Jiang⁴  Baoxu Liu⁴ 

[1] Beijing Electronic Science and Technology Institute, 102627, Beijing, China;College of Information Engineering, Capital Normal University, 100048, Beijing, China;Institute of Information Engineering, Chinese Academy of Sciences, 100093, Beijing, China;Institute of Information Engineering, Chinese Academy of Sciences, 100093, Beijing, China;School of Cyber Security, University of Chinese Academy of Sciences, 100029, Beijing, China;

关键词: TTPs;    Threat intelligence;    Natural language processing (NLP);    Advanced persistent threat (APT);   

DOI  :  10.1186/s42400-021-00106-5

来源: Springer

【 摘 要 】

TTPs (Tactics, Techniques, and Procedures), which represent an attacker’s goals and methods, are the long period and essential feature of the attacker. Defenders can use TTP intelligence to perform the penetration test and compensate for defense deficiency. However, most TTP intelligence is described in unstructured threat data, such as APT analysis reports. Manually converting natural language TTPs descriptions to standard TTP names, such as ATT&CK TTP names and IDs, is time-consuming and requires deep expertise. In this paper, we define the TTP classification task as a sentence classification task. We annotate a new sentence-level TTP dataset with 6 categories and 6061 TTP descriptions from 10761 security analysis reports. We construct a threat context-enhanced TTP intelligence mining (TIM) framework to mine TTP intelligence from unstructured threat data. The TIM framework uses TCENet (Threat Context Enhanced Network) to find and classify TTP descriptions, which we define as three continuous sentences, from textual data. Meanwhile, we use the element features of TTP in the descriptions to enhance the TTPs classification accuracy of TCENet. The evaluation result shows that the average classification accuracy of our proposed method on the 6 TTP categories reaches 0.941. The evaluation results also show that adding TTP element features can improve our classification accuracy compared to using only text features. TCENet also achieved the best results compared to the previous document-level TTP classification works and other popular text classification methods, even in the case of few-shot training samples. Finally, the TIM framework organizes TTP descriptions and TTP elements into STIX 2.1 format as final TTP intelligence for sharing the long-period and essential attack behavior characteristics of attackers. In addition, we transform TTP intelligence into sigma detection rules for attack behavior detection. Such TTP intelligence and rules can help defenders deploy long-term effective threat detection and perform more realistic attack simulations to strengthen defense.

【 授权许可】

CC BY