Hashchain-Based Bot Hub Monitoring and Repair
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack efficient methods to monitor and repair virtual bots in a hashchain-based distributed bot hub, leading to potential disruptions and security risks due to abnormal or malicious bot behavior, which can impact workflow tasks and compromise sensitive information.
Innovation Solution
A computing platform that uses hashchains and machine learning to monitor and evaluate virtual bot activity, identifying anomalous behavior, quarantining affected bots, and executing repair processes to maintain workflow integrity and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual bots are deployed to process workflow tasks in a distributed bot hub, then productivity and automation capability are improved, but system reliability and security deteriorate due to potential abnormal or malicious bot behavior
Solution Approach 1:
The system performs preliminary actions by computing hashchains for each bot before workflow execution. These hashchains serve as pre-established verification mechanisms that enable later detection of abnormal bot behavior without disrupting workflow productivity.
Solution Approach 2:
The system implements continuous feedback through monitoring bot activities against their computed hashchains. When deviations are detected, the system provides feedback by identifying anomalous behavior and initiating repair processes, thereby maintaining reliability while preserving productivity.
2Reliability
If monitoring mechanisms are implemented to detect abnormal bot behavior, then system security and reliability are improved, but device complexity and operational overhead increase
Solution Approach 1:
The monitoring system utilizes the bots' own hashchains as verification credentials. Each bot carries its own verification mechanism in the form of a hashchain, eliminating the need for external complex verification infrastructure and reducing overall system complexity.
Solution Approach 2:
The system changes the parameter of bot identification from simple IDs to cryptographic hashchains. This parameter change enables robust verification and anomaly detection while maintaining computational efficiency, as hashchain verification can be performed with standard cryptographic operations.
3Object-affected harmful factors
If anomalous bot behavior is detected and addressed through quarantine and repair processes, then system security is improved, but loss of time and productivity occur during repair operations
Solution Approach 1:
The system extracts problematic bots from the operational workflow by quarantining them. This separation allows repair operations to be performed on isolated bots without affecting the overall workflow continuity, minimizing productivity loss while maintaining security.
Solution Approach 2:
The system performs preliminary computations of hashchains that enable rapid anomaly detection. This preliminary preparation reduces the time required to identify and respond to malicious bot behavior, minimizing the downtime associated with detection and response operations.
Data Source
AI summary
Aspects of the disclosure relate to monitoring, evaluating, and repairing bots in a hashchain-based distributed bot hub that process a workflow. In some embodiments, a computing platform may receive workflow information associated with performing a first workflow that includes executing one or more tasks using a plurality of virtual bots, identify a plurality of bots to process the first workflow, and determine, using a machine learning model, an arrangement of bot hubs in which each bot hub includes at least one bot and bots within a common bot hub share metadata while executing the first workflow process. Thereafter, the computing platform may send the determined arrangement of bot hubs to a bot orchestrator on a virtual bot host server to cause the bot orchestrator to instantiate the bots to form the determined arrangement of bot hubs and to process tasks from the first workflow using the at least one bot.