Publications

Abstract not provided.

Abstract not provided.

This report summarizes the activities performed as part of the Science and Engineering of Cybersecurity by Uncertainty quantification and Rigorous Experimentation (SECURE) Grand Challenge LDRD project. We provide an overview of the research done in this project, including work on cyber emulation, uncertainty quantification, and optimization. We present examples of integrated analyses performed on two case studies: a network scanning/detection study and a malware command and control study. We highlight the importance of experimental workflows and list references of papers and presentations developed under this project. We outline lessons learned and suggestions for future work.

Concerns about cyber threats to space systems are increasing. Researchers are developing intrusion detection and protection systems to mitigate these threats, but sparsity of cyber threat data poses a significant challenge to these efforts. Development of credible threat data sets are needed to overcome this challenge. This paper describes the extension/development of three data generation algorithms (generative adversarial networks, variational auto-encoders, and generative algorithm for multi-variate timeseries) to generate cyber threat data for space systems. The algorithms are applied to a use case that leverages the NASA Operational Simulation for Small Satellites (NOS$^{3})$ platform. Qualitative and quantitative measures are applied to evaluate the generated data. Strengths and weaknesses of each algorithm are presented, and suggested improvements are provided. For this use case, generative algorithm for multi-variate timeseries performed best according to both qualitative and quantitative measures.

Concerns about cyber threats to space systems are increasing. Researchers are developing intrusion detection and protection systems to mitigate these threats, but sparsity of cyber threat data poses a significant challenge to these efforts. Development of credible threat data sets are needed to overcome this challenge. This paper describes the extension/development of three data generation algorithms (generative adversarial networks, variational auto-encoders, and generative algorithm for multi-variate timeseries) to generate cyber threat data for space systems. The algorithms are applied to a use case that leverages the NASA Operational Simulation for Small Satellites (NOS$^{3})$ platform. Qualitative and quantitative measures are applied to evaluate the generated data. Strengths and weaknesses of each algorithm are presented, and suggested improvements are provided. For this use case, generative algorithm for multi-variate timeseries performed best according to both qualitative and quantitative measures.

Abstract not provided.

Abstract not provided.