no code implementations • 12 Dec 2023 • Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Gina Adam, Nathaniel D. Bastian, Tian Lan
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e. g., network intrusion detection from a sequence of arriving packets.
no code implementations • 27 Nov 2023 • Jingdi Chen, Lei Zhang, Joseph Riem, Gina Adam, Nathaniel D. Bastian, Tian Lan
Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in high-speed communication networks are challenging due to the high computation time and resource requirements of Deep Neural Networks (DNNs), as well as lack of explainability.
1 code implementation • 7 Aug 2023 • Jingdi Chen, Tian Lan, Carlee Joe-Wong
This result enables us to recast multi-agent communication into a novel online clustering problem over the local observations at each agent, with messages as cluster labels and the upper bound on the return gap as clustering loss.
no code implementations • 21 Jul 2023 • Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal
Our key idea is to approximate the joint state space as a Kronecker graph, based on which we can directly estimate its Fiedler vector using the Laplacian spectrum of individual agents' transition graphs.
no code implementations • 20 Jan 2022 • Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal
Covering skill (a. k. a., option) discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph.