Yun Shen

In Proceedings of the 28th USENIX Security Symposium (USENIX 2019)
We present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve.

In Proceedings of the 23rd ACM Conference on Computer and Communications Security (ACM Sigsac 2016)
In this paper, we introduce four categories of email profiling features that capture various characteristics of spear phishing emails. Building on these features, we implement and evaluate an affinity graph-based semi-supervised learning model for campaign attribution and detection.

In Proceedings of the 2nd IEEE International Conference on Big Data 2014 (IEEE BigData 2014)
We introduce a new framework called MR-TRIAGE leveraging multi-criteria data clustering (MCDC) to perform scalable data clustering on large security data sets and further implement a set of efficient algorithms in a 3-stage MapReduce paradigm.

Journal of Neural Computing and Applications, Volume 25, Issue 7–8, December 2014
We proposed a new approach which applies the mass assignment-based fuzzy association rules mining (MASS-FARM) algorithm to Twitter data analysis, for the first time, to automatically extract useful and meaningful knowledge from large-scale data set.

In Proceedings of the 31st ACM/SIGAPP Symposium on Applied Computing (ACM SAC 2016)
We proposed the first quantitative analysis of mobile devices from the perspective of comparing rooted devices to non-rooted devices. We have attempted to map high level thoughts about the characteristics of users who root their devices to the low-level data at our disposal.

In Proceedings of the 28th USENIX Security Symposium (USENIX 2019)
We present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve.

In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016)
We propose to encode the weakly supervised information in PU learning tasks into pairwise constraints between training in-stances. Violation of pairwise constraints are measured and incorporated into a partially supervised graph embedding model.

In Proceedings of the 25th ACM Conference on Computer and Communications Security (ACM CCS 2018)

In Proceeding of the 14th ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS 2019)
We present a longitudinal measurement of malicious file distribution on the Web.

In Proceedings of the ENISA Annual Privacy Forum (APF 2019)
We devise a concise, informative IoT labelling scheme to convey high-level security and privacy facts about an IoT device to the consumers so as to raise their security and privacy awareness.

In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019)
In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. The focus is to find strategies to organize distributed agents to jointly select a compact subset of data that can be used to train a global model. The global model should achieve nearly the same performance as if the central learner had access to all the data, but the central learner only has access to the selected subset, and each agent only has access to their own data. The goal of this research is to find good strategies to train global models while giving some control back to agents.