## 3 Sharing ML Models as Cyber Threat Intelligence
### Sharing ML Models as Threat Intelligence
To illustrate, CICFlowMeter pre-processed each adversarial PCAP file with the modified HTTP response in about 695 ms, and Zeek did the same job in about 296 ms.

To summarize, the computational overhead to cryptographically protect the ML models and associated artifact is relatively small compared to the actual time to pre-process the data, deploy, and evaluate the ML models, for example, to find false positives, false negatives, or identify adversarial ML threats as outlined in our MISP taxonomy of Figure 4:8

## 5 Conclusions

In this work, we proposed a solution to complement the sharing of attribute-based IoC with machine learning based threat detection, as the former have been shown to lose value over time. When using AI to strengthen cyber security applications, and threat detection frameworks in particular, we must account for the risks and vulnerabilities that machine learning models may impose. We demonstrated this for a DDoS network security use case, comparing different ML and DL models, and their robustness against adversarial examples that aim to evade detection. To address these challenges, our solution proposed a new object template to simplify the sharing of ML models and associated artifacts across different instances of the well-established MISP threat intelligence sharing platform. We additionally defined a new MISP taxonomy to tag this threat intelligence with adversarial ML threats. Last but not least, to prevent unauthorized access to potentially sensitive ML models and input samples, we leveraged our previous work TATIS to encrypt this threat intelligence with CP-ABE so that only authorized members of a community can decrypt this information. To assist security analysts, we implemented these encryption, decryption, and processing capabilities as extensions for the TheHive and Cortex security incident response frameworks. We carried out a performance analysis, indicating that the proposed solution is practically feasible. As future work, we will investigate to what extent threats against shared ML models can be automatically annotated, also for cyber security use cases beyond IDS, and how explainable AI frameworks could be plugged into our solution to help enrich shared ML models. Figure 4:9 Performance impact of decrypting, deploying, and processing ML models. **Author Contributions:** Conceptualization, methodology, software, and validation, D.P. ; writing--original draft preparation, D.P. ; writing--review and editing, D.P. ; supervision, D.P. and W.J. ; project administration, D.P. ; funding acquisition, W.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research is partially funded by the Research Fund KU Leuven, by the Flemish Research Programme Cybersecurity, and by the European H2020-SU-ICT-03-2018 Project No. 830929 CyberSec4 Europe ([[https://cybersecdeurope.eu](https://cybersecdeurope.eu)]([https://cybersecdeurope.eu](https://cybersecdeurope.eu)) accessed on 24 February 2021). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The CSE-CIC-IDS2018 dataset analysed during this study is available at [[https://www.unb.ca/cic/datasets/ids-2018.html](https://www.unb.ca/cic/datasets/ids-2018.html)]([https://www.unb.ca/cic/datasets/ids-2018.html](https://www.unb.ca/cic/datasets/ids-2018.html)), accessed on 24 February 2021. **Acknowledgments:** We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

* [1] [PERSON]; [PERSON]; [PERSON]; [PERSON] Integrating Threat Intelligence to Enhance an Organization's Information Security Management. In Proceedings of the 13 th International Conference on Availability, Reliability and Security, ARES 2018, Hamburg, Germany, 27-30 August 2018.