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您的检索式：作者名="Peian Yang"
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| 1 | TriCTI:an actionable cyber threat intelligence discovery system via trigger-enhanced neural network显示文摘The cybersecurity report provides unstructured actionable cyber threat intelligence(CTI)with detailed threat attack procedures and indicators of compromise(IOCs),e.g.,malware hash or URL(uniform resource locator)of command and control server.The actionable CTI,integrated into intrusion detection systems,can not only prioritize the most urgent threats based on the campaign stages of attack vectors(i.e.,IOCs)but also take appropriate mitigation measures based on contextual information of the alerts.However,the dramatic growth in the number of cybersecurity reports makes it nearly impossible for security professionals to find an efficient way to use these massive amounts of threat intelligence.In this paper,we propose a trigger-enhanced actionable CTI discovery system(TriCTI)to portray a relationship between IOCs and campaign stages and generate actionable CTI from cybersecurity reports through natural language processing(NLP)technology.Specifically,we introduce the“campaign trigger”for an effective explanation of the campaign stages to improve the performance of the classification model.The campaign trigger phrases are the keywords in the sentence that imply the campaign stage.The trained final trigger vectors have similar space representations with the keywords in the unseen sentence and will help correct classification by increasing the weight of the keywords.We also meticulously devise a data augmentation specifically for cybersecurity training sets to cope with the challenge of the scarcity of annotation data sets.Compared with state-of-the-art text classification models,such as BERT,the trigger-enhanced classification model has better performance with accuracy(86.99%)and F1 score(87.02%).We run TriCTI on more than 29k cybersecurity reports,from which we automatically and efficiently collect 113,543 actionable CTI.In particular,we verify the actionability of discovered CTI by using large-scale field data from VirusTotal(VT).The results demonstrate that the threat intelligence provided by VT lacks a part of the threat context for IOCs,such as the Actions on Objectives campaign stage.As a comparison,our proposed method can completely identify the actionable CTI in all campaign stages.Accordingly,cyber threats can be identified and resisted at any campaign stage with the discovered actionable CTI. | Jian Lu Junjie Yan Jun Jiang Yitong He Xuren Wang Zhengwei jiang Peian Yang Ning Li | 2022 | Cybersecurity2022，5，3： | 1 |
| 2 | TIM: threat context-enhanced TTP intelligence mining on unstructured threat data显示文摘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. | Yizhe You Jun Jiang Zhengwei Jiang Peian Yang Baoxu Liu Huamin Feng Xuren Wang Ning Li | 2022 | Cybersecurity2022，5，2： | 1 |
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