Writing Pattern Profiles for Faster Impersonation Detection
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Solution Overview
Problem
Existing systems lack effective methods to detect impersonation attacks by analyzing writing patterns and narrative styles in digital communications, which are crucial for preventing phishing attempts and enhancing security in corporate or business environments.
Innovation Solution
A computer-implemented system uses natural language processing and ensemble clustering algorithms to create a writing profile model that compares incoming communications against stored profiles, flagging potential impersonation attacks based on confidence thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If writing pattern analysis is performed on all incoming communications, then detection accuracy of impersonation attacks is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training writing profile models on historical data and storing them for rapid comparison. When a new communication arrives, the system compares it against pre-existing profiles rather than analyzing everything from scratch, significantly reducing processing time while maintaining high detection accuracy.
Solution Approach 2:
The system creates a copy of the analysis process by using pre-computed writing profiles as templates. Instead of performing complete re-analysis, the system uses these profile copies to quickly assess new communications, flagging only those that deviate significantly from established patterns.
2Reliability
If multiple clustering algorithms are used to analyze writing patterns, then detection reliability is improved, but system complexity increases
Solution Approach 1:
The system merges multiple clustering algorithms into a unified writing profile model that integrates their strengths. By combining the results of different clustering approaches, the system achieves higher reliability in detecting writing patterns while managing complexity through a cohesive, integrated model structure.
Solution Approach 2:
The writing profile model serves multiple functions simultaneously: it clusters writing patterns, identifies narrative styles, and generates detection profiles. This multi-functionality reduces the need for separate complex systems while maintaining high reliability through the unified approach.
3Adaptability or versatility
If writing profiles are updated continuously with new data, then adaptability to new attack patterns is improved, but computational resources and processing time increase
Solution Approach 1:
The system uses periodic action by updating writing profiles at scheduled intervals or when significant changes are detected, rather than continuously processing all incoming data. This allows the system to adapt to new attack patterns while conserving computational resources by performing updates only when necessary.
Solution Approach 2:
The system implements feedback mechanisms where detection results and new communications are used to refine and update writing profiles. This feedback loop enables continuous adaptation to new patterns while managing computational resources by focusing updates on the most impactful data points.
Data Source
AI summary
Embodiments determine a set of writing patterns of at least one data communication; determine a narrative style on the set of writing patterns of the at least one data communication; provide a writing profile model by training on the narrative style and the set of writing patterns; compare a new data communication to the trained writing profile model; and flag the new data communication based on the comparison being over a threshold level.