Chat Mining Tool for Social Engineering Detection
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Solution Overview
Problem
Current methods fail to effectively detect social engineering attempts in chat sessions, posing a risk to information security as they rely on manual detection and are prone to missing urgent or manipulative tactics.
Innovation Solution
A chat mining tool equipped with a theme detecting engine, tracking engine, and classification engine that analyzes chat session transcripts, counts keyword occurrences, and assigns sessions to groups based on detected themes and word frequencies to identify potential social engineering attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual detection methods are used to identify social engineering attempts, then the system is simple to implement, but the detection speed and reliability are insufficient
Solution Approach 1:
The chat mining tool segments the detection process into three distinct functional modules: theme detecting engine (identifies discussion topics), tracking engine (monitors keyword frequencies), and classification engine (categorizes chat sessions). This segmentation enables comprehensive analysis while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary processing layer between the chat transcript and the detection outcome. The theme detecting engine, tracking engine, and classification engine collectively form an intermediary system that transforms raw chat data into structured analysis results, improving detection reliability without requiring direct manual intervention.
2Productivity
If automated analysis is implemented to detect social engineering attempts, then the detection speed and accuracy improve, but the system complexity increases
Solution Approach 1:
The chat mining tool implements self-service automation where the system independently performs theme detection, keyword tracking, and classification without human intervention. The automated engines process chat transcripts, identify social engineering patterns, and generate detection results autonomously, maximizing detection speed and productivity.
Solution Approach 2:
The patent replaces manual mechanical detection processes with automated computational engines. Instead of human analysts manually reviewing chats, the theme detecting engine, tracking engine, and classification engine automatically analyze transcripts using algorithmic processes, dramatically improving detection speed while managing complexity through software-based solutions.
3Measurement precision
If comprehensive keyword tracking is performed across all themes, then the detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-defining multiple themes and associated keywords before chat analysis begins. The theme detecting engine is pre-configured with thematic categories and their corresponding keywords, enabling rapid matching and classification during actual chat processing without requiring real-time computation of all possible keyword combinations.
Solution Approach 2:
The patent applies partial action by focusing keyword tracking on specific themes and keywords relevant to each chat session rather than uniformly analyzing all possible keywords across all chats. The tracking engine monitors keyword frequencies selectively based on detected themes, achieving high detection accuracy while reducing unnecessary processing of irrelevant data.
Data Source
AI summary
A method comprises counting, in a transcript of a chat session between a first user and a second user, for each theme of a plurality of themes, a number of occurrences of each keyword of a plurality of keywords assigned to a theme of a plurality of themes. The method further comprising identifying one or more themes of the chat session based on the number of occurrences of each keyword, counting the number of occurrences of a word of a first set of words and a word of a second set of words in the transcript, and assigning the transcript into a first group or a second group based on the one or more identified themes and the number of occurrences of first words and second words.