Episodic Social Network for Malware Detection Visualization
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Solution Overview
Problem
Complex systems such as malware detection and healthcare management require efficient and effective coordination of resources and processes, which are often multifaceted and difficult for humans to manage in real-time, necessitating a unified dynamic machine intelligence that can analyze interactions and allocate resources while being understandable by humans.
Innovation Solution
The implementation of Episodic Social Network (ESN) theory, which models systems as a series of affinity groups connected by conditional situation blocks, allowing for the representation of complex interactions and processes in a human-comprehensible format, enabling efficient management and coordination of resources across multiple roles and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processes are used for malware detection and removal, then processing speed and efficiency are improved, but understandability and controllability by humans deteriorate
Solution Approach 1:
The patent introduces an intermediary layer between the automated malware detection system and human operators. This intermediary translates complex automated processes into understandable visual representations showing malware detection status, removal actions, and system state changes, allowing humans to monitor and control automated processes without needing to understand the underlying complexity
Solution Approach 2:
The patent segments the complex automated malware removal process into distinct, understandable phases and components. Each phase (detection, analysis, removal, verification) is presented separately with clear indicators, allowing human operators to understand and control specific aspects of the process while automation handles the overall execution
2Reliability
If multifaceted remedial routines are applied to handle multiple simultaneous malware infections, then detection accuracy and completeness are improved, but process complexity increases
Solution Approach 1:
The patent merges multiple remedial routines and detection methods into a unified automated process that handles multiple malware infections simultaneously. The system combines signature-based detection, behavior analysis, and remedial actions into a single coordinated workflow, reducing the apparent complexity for human operators while maintaining comprehensive coverage
Solution Approach 2:
The patent creates a universal remedial framework that can handle multiple types of malware infections through a single multifaceted process. The system is designed to detect, analyze, and remove various malware types (viruses, worms, trojans, ransomware) using a unified approach, eliminating the need for separate complex processes for each malware type
3Reliability
If comprehensive monitoring and coordination of multiple networked computing devices is implemented, then system-wide detection capability is improved, but communication overhead and coordination complexity increase
Solution Approach 1:
The patent extracts and centralizes the coordination and communication functions in a hub system that manages multiple networked computing devices. The hub consolidates monitoring data from all devices, coordinates remedial actions, and manages communication protocols, reducing redundant communication overhead while maintaining system-wide detection capability
Solution Approach 2:
The patent implements feedback mechanisms where the hub system continuously monitors the state of networked devices, detects malware infections, coordinates remedial actions, and verifies removal success. This closed-loop feedback system improves system-wide detection by ensuring all devices are monitored and coordinated efficiently, reducing communication overhead through optimized feedback cycles
Data Source
AI summary
Systems and methods for management of data files using a plurality of interconnected operations associated with a plurality of roles are provided. A method involves receiving, from a user terminal, a request to access a portion of the plurality of interconnected operations corresponding to one of the plurality of roles, obtaining a human representation of the portion, and transmitting the human representation to the user terminal for display thereon. The human representation (i.e., an Episodic Social Network representation) is a spatial arrangement one or more affinity groups blocks interconnected via one or more conditional situation blocks, where each of the affinity groups represents a non-exclusive data file classification associated with a set of temporal and non-temporal characteristics and where each of the conditional situation blocks defines a set of conditions for transferring the data file from one of the affinity groups to another of the affinity groups.