Publications

The objective of this project was to develop a novel capability to generate synthetic data sets for the purpose of training Machine Learning (ML) algorithms for the detection of malicious activities on satellite systems. The approach experimented with was to a) generate sparse data sets using emulation modeling and b) enlarge the sparse data using Generative Adversarial Networks (GANs). We based our emulation modeling on the Open Source NASA Operational Simulator for Small Satellites (NOS3) developed by the Katherine Johnson Independent Verification and Validation (IV&V) program in West Virginia. Significant new capabilities on NOS3 had to be developed for our data set generation needs. To expand these data sets for the purpose of training ML, we experimented with a) Extreme Learning Machines (ELMs) and b) Wasserstein-GANs (WGAN-GP).

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Wireless systems and networks have experienced rapid growth over the last decade with the advent of smart devices for everyday use. These systems, which include smartphones, vehicular gadgets, and internet-of-things devices, are becoming ubiquitous and ever-more important. They pose interesting research challenges for design and analysis of new network protocols due to their large scale and complexity. In this work, we focus on the challenging aspect of simulating the inter-connectivity of many of these devices in wireless networks. The quantitative study of large scale wireless networks, with counts of wireless devices in the thousands, is a very difficult problem with no known acceptable solution. By necessity, simulations of this scale have to approximate reality, but the algorithms employed in most modern-day network simulators can be improved for wireless network simulations. In this report, we present advances that we have made and propositions for continuation of progress towards a framework for high fidelity simulations of wireless networks. This work is not complete in that a final simulation framework tool is yet to be produced. However, we highlight the major bottlenecks and address them individually with initial results showing enough promise.

Abstract not provided.