Abstract: A framework for interfacing a blockchain-based ground system with flight software and satellite orbit analysis applications is disclosed. A blockchain application (e.g., a web application) for secure management of satellites has three components—a client, a server, and a private and permissioned-based blockchain network (e.g., a Hyperledger Fabric™ network). The client allows users to configure satellite parameters. The server facilitates the communication between the client and the blockchain. The blockchain allows the secure management and storage of satellite configuration data on blockchain ledgers. For instance, SECCON provides a blockchain-based framework that can be a potential candidate for next generation ground system applications. SECCON allows secure configuration management of satellites, and has been interfaced with OpenSatKit, a software tool kit that can interact with the cFS.

Abstract: Cyberattacks are rampant and can play a major role in modern warfare, particularly on a widely adopted platforms such as the MIL-STD-1553 standard. To protect a 1553 communication bus system from attacks, a trained statistical or machine learning model can be used to monitor commands from a bus controller of the 1553 communication bus system. The statistical and/or machine learning model can be trained to recognize communication anomalies based at least on the probability distribution of patterns of one or more commands. The statistical model can be stochastic model such as a Markov chain that describes a sequence of possible commands in which the probability of each command depends on the occurrence of a group of one or more commands.

Abstract: Cyberattacks are rampant and can play a major role in modern warfare, particularly on a widely adopted platforms such as the MIL-STD-1553 standard. To protect a 1553 communication bus system from attacks, a trained statistical or machine learning model can be used to monitor commands from a bus controller of the 1553 communication bus system. The statistical and/or machine learning model can be trained to recognize communication anomalies based at least on the probability distribution of patterns of one or more commands. The statistical model can be stochastic model such as a Markov chain that describes a sequence of possible commands in which the probability of each command depends on the occurrence of a group of one or more commands.