Integrating Deep Reinforced Learning and Robotic Process Assessment in Blockchain Digital Transformation for Autonomous Cybersecurity

摘要

View Video Presentation: https://doi.org/10.2514/6.2021-0662.vid The term Blockchain Technology (BT) refers to information technology (IT) where data is stored in numerous blocks using multiple distributed servers. Through digital automation the data is refreshed periodically at a frequency set by data architects and network designers. The servers are sometimes on the public internet, but in the aerospace world it is more within private intranet of authorized users to maintain privacy and security. A Blockchain network registers the components within aerospace echo system in a digital ledger accompanied by relevant data. It is virtually immutable. The data residing within BT cannot be changed without the awareness of the collaborative parties. Even if a hacker manages to penetrate one block, the overall integrity of the data would remain intact since the data is distributed among thousands of blocks containing cryptographically protected digital ledgers. Deep reinforced learning (DRL) is a machine learning (ML) process based on reinforced learning (RL) and neural networks (NN). DRL training algorithms adjusts its actions dynamically motivated by a system of reward and punishment. The DRL algorithm learns by interacting with its dynamic environment. The DRL software agents receive rewards for correct actions and penalties for performing incorrectly. These agents learn without human intervention by targeting maximizing the reward and minimizing the penalty. Robotic process automation (RPA) is a business process automation technology based on software agents. It can trigger automated agent-based actions. When RPA and DRL are combined, system operates software agents that can learn in dynamic environment automatically without human interventions. They act as autonomous agents that learns continuously and operates cyber infrastructure autonomously. In this paper we show the implementation process and the benefits of integrating deep reinforced learning and robotic process automation in Blockchain digital transformation for autonomous cybersecurity.