Multi-Channel Unauthorized Activity Detection via ML Structuring
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Solution Overview
Problem
Identifying and responding to potential unauthorized activities across multiple communication channels is challenging due to the complexity of threats originating from various sources, including email, chat, instant messaging, and web activity, which often require immediate action to mitigate impacts.
Innovation Solution
A dynamic unauthorized activity detection system utilizing machine learning to analyze data from multiple channels, transform unstructured data into a structured format, and evaluate triggering content for potential unauthorized activities, generating alerts and modifying communication channel functionality as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to evaluate data from multiple channels, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the detection process into separate modules: data collection from multiple channels, data transformation from unstructured to structured format, machine learning evaluation, and alert generation. This segmentation allows each module to be optimized independently while working together to achieve high detection accuracy across multiple communication channels.
Solution Approach 2:
The patent introduces a data transformation layer as an intermediary between raw data from multiple channels and the machine learning evaluation layer. This intermediary converts unstructured data into a standardized structured format, simplifying the subsequent analysis process and reducing the complexity burden on the machine learning models.
2Reliability
If data from multiple communication channels is analyzed, then threat identification capability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary data transformation and structuring of data from multiple channels before it reaches the machine learning evaluation stage. By preparing and organizing the data in advance, the subsequent threat identification process is accelerated, reducing overall processing time while maintaining comprehensive multi-channel analysis.
3Measurement precision
If triggering content is evaluated across channels, then unauthorized activity detection is improved, but false positives may increase
Solution Approach 1:
The system incorporates feedback mechanisms that continuously evaluate detected triggering content across multiple channels and adjust detection parameters accordingly. By analyzing patterns and outcomes of previous detections, the system refines its ability to distinguish between actual unauthorized activity and false positives, improving overall detection reliability.
Data Source
AI summary
Systems for dynamically detecting unauthorized activity are provided. A system may receive data from one or more computing devices associated with one or more different channels of communication (e.g., email, telephone, instant messaging, internet browsing, and the like). The received data may be formatted or transformed from an unstructured format to a structured format for further analysis and evaluation. In some arrangements, machine learning may be used to determine whether triggering content was identified in data received from the one or more systems and to evaluate the identified triggering content to determine whether the content, alone or in combination with triggering content from other channels of communication, may indicate an occurrence of unauthorized activity. If so, the identified occurrence may be evaluated to determine whether a false positive has occurred. If a false positive has not occurred, an alert or notification may be generated and/or operation or functionality one or more communication channels may be modified.