Research & Publications

Research Projects

Lawrence Livermore National Laboratory - I am collaborating with the civilian cybersecurity group, working on power grid security research

Dr. Wenke Lee's lab - Currently, I am working on cybersecurity related to the power grid

Dr. Marie desJardin's lab - During the last year at UMBC, I worked in the MAPLE lab. I focused on Hierarchical Reinforcement Learning, helping to design and implemement the R-AMDP agent. I co-authored 2 papers for RLDM and ICAPS IntEX .


Citation Link
Joey Allen, Zheng Yang, Matthew Landen, Raghav Bhat, Harsh Grover, Andrew Chang, Yang Ji, Roberto Perdisci, and Wenke Lee. 2020. Mnemosyne: An Effective and Efficient Postmortem Watering Hole Attack Investigation System. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (CCS '20). Association for Computing Machinery, New York, NY, USA, 787–802. DOI: PDF
Winder, J., Milani, S., Landen, M., Oh, E., Parr, S., Squire, S., & Matuszek, C. (2020, April). Planning with Abstract Learned Models While Learning Transferable Subtasks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 06, pp. 9992-10000). PDF
Joey Allen, Matthew Landen, Sanya Chaba, Yang Ji, Simon Chung, Wenke Lee “Improving Accuracy of Android Malware Detection with Lightweight Contextual Awareness” In Annual Computer Security Applications Conference, 2018 PDF
John Winder, Shawn Squire, Matthew Landen, Stephanie Milani and Marie desJardins "Towards Planning With Hierarchies of Learned Markov Decision Processes" In ICAPS-2017 Integrated Execution of Planning and Acting Workshop, pg 50-53, 2017 PDF
Shawn Squire, John Winder, Matthew Landen, Stephanie Milani, Marie desJardins "R-AMDP: Model-Based Learning for Abstract Markov Decision Process Hierarchies" In The Multi-disciplinary Conference on Reinforcement Learning and Decision Making 2017, 2017 PDF