Charles A. Kamhoua

TL;DR: This work proposes a multi-agent Deep Reinforcement Learning-based network slicing technique that can help determine two key resource management decisions: (1) link bandwidth allocation to meet Quality-of-Service requirements and (2) the frequency of triggering IP shuffling as an MTD operation not to hinder service availability by maintaining normal system operations.

TL;DR: A feature-engineering based signal classification toolbox which implements RF signal detection, Cyclostationary Features Extraction and Feature engineering, Automatic Modulation Recognition to automatically recognize modulation as well as sub-modulation types of the received signal is proposed.

TL;DR: This work proposes an abstraction technique that addresses the issue of the curse of dimensionality by projecting high-dimensional beliefs to characteristic vectors of significantly lower dimension (e.g., marginal probabilities) and proposes a novel compact representation of the uncertainty in partially observable stochastic games.

TL;DR: This paper introduced a new approach, with which an integrative information feature set is determined to identify abnormal network activities using wavelet transformation, and uses all attributes information to extract features and to design a reliable learning model to detect abnormal activities by reducing false positives.