Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii

Airborne Directional Networking: Topology Control Protocol Design

This research identifies and evaluates the impact of several architectural design choices in relation to airborne networking in contested environments related to autonomous topology control. Using simulation, we evaluate topology reconfiguration effectiveness using classical performance metrics for different point-to-point communication architectures. Our attention is focused on the design choices which have the greatest impact on reliability, scalability, and performance. In this work, we discuss the impact of several practical considerations of airborne networking in contested environments related to autonomous topology control modeling. Using simulation, we derive multiple classical performance metrics to evaluate topology reconfiguration effectiveness for different point-to-point communication architecture attributes for the purpose of qualifying protocol design elements

Sponsored Programs Annual Report / Graduate School of Engineering and Applied Sciences, Fiscal Year 2006

The Graduate School of Engineering and Applied Sciences (GSEAS) at the Naval Postgraduate School in Monterey, California provides graduate education leading to the master of science, engineer, doctor of philosophy, and doctor of engineering degrees. This FY2006 report provides brief program information ; Sponsored Program expenditures by type of activity, by sponsor and by department. These departments are: Applied Mathematics (MA), Electrical and Computer Engineering (ECE) ; Electrical and Computer Engineering ; Mechanical and Astronautical Engineering (MAE) ; Meteorology (MR) ; Oceanography (OC) ; Physics (PH) ; Systems Engineering (SE) ; and the Space Systems Academic Group

Ethical Control of Unmanned Systems: lifesaving/lethal scenarios for naval operations

Prepared for: Raytheon Missiles & Defense under NCRADA-NPS-19-0227This research in Ethical Control of Unmanned Systems applies precepts of Network Optional Warfare (NOW) to develop a three-step Mission Execution Ontology (MEO) methodology for validating, simulating, and implementing mission orders for unmanned systems. First, mission orders are represented in ontologies that are understandable by humans and readable by machines. Next, the MEO is validated and tested for logical coherence using Semantic Web standards. The validated MEO is refined for implementation in simulation and visualization. This process is iterated until the MEO is ready for implementation. This methodology is applied to four Naval scenarios in order of increasing challenges that the operational environment and the adversary impose on the Human-Machine Team. The extent of challenge to Ethical Control in the scenarios is used to refine the MEO for the unmanned system. The research also considers Data-Centric Security and blockchain distributed ledger as enabling technologies for Ethical Control. Data-Centric Security is a combination of structured messaging, efficient compression, digital signature, and document encryption, in correct order, for round-trip messaging. Blockchain distributed ledger has potential to further add integrity measures for aggregated message sets, confirming receipt/response/sequencing without undetected message loss. When implemented, these technologies together form the end-to-end data security that ensures mutual trust and command authority in real-world operational environments—despite the potential presence of interfering network conditions, intermittent gaps, or potential opponent intercept. A coherent Ethical Control approach to command and control of unmanned systems is thus feasible. Therefore, this research concludes that maintaining human control of unmanned systems at long ranges of time-duration and distance, in denied, degraded, and deceptive environments, is possible through well-defined mission orders and data security technologies. Finally, as the human role remains essential in Ethical Control of unmanned systems, this research recommends the development of an unmanned system qualification process for Naval operations, as well as additional research prioritized based on urgency and impact.Raytheon Missiles & DefenseRaytheon Missiles & Defense (RMD).Approved for public release; distribution is unlimited