Personalized Phishing Warnings to Reduce User Habituation
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Solution Overview
Problem
Users become habituated to frequency security warnings, reducing their efficacy in detecting phishing attacks, as existing anti-phishing measures struggle to adapt to evolving phishing techniques.
Innovation Solution
A personalized warning system that analyzes user interactions and message content to generate susceptibility scores, integrating local and global models to determine when to output warnings based on individual and demographic susceptibilities, thereby reducing habituation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security warnings are output frequently to users, then phishing detection coverage is improved, but user habituation occurs reducing warning efficacy
Solution Approach 1:
The system implements personalized warning strategies tailored to each user's susceptibility profile, demographic characteristics, and interaction history. Instead of uniform warning frequency for all users, the system adapts warning delivery to individual needs, ensuring high-risk users receive more warnings while low-risk users receive fewer warnings, thereby maintaining efficacy across the user base.
Solution Approach 2:
The warning system dynamically adjusts its behavior based on real-time analysis of user interactions, message characteristics, and susceptibility scores. The system continuously learns from user responses to warnings and modifies future warning delivery accordingly, transforming from a static to a dynamic adaptation process that prevents habituation while maintaining detection coverage.
2Reliability
If personalized warning analysis is implemented, then warning efficacy is improved, but system complexity increases
Solution Approach 1:
The system divides the complex task of phishing detection into separate specialized models: local models trained on individual user interaction data, global models trained on aggregate population data, and demographic models. This segmentation allows each model to focus on specific aspects of the problem, improving overall efficacy while managing complexity through modular architecture.
Solution Approach 2:
The system introduces susceptibility scores as an intermediary metric that synthesizes complex user characteristics, demographic factors, and message features into a single actionable value. This intermediary simplifies the decision-making process for warning delivery while capturing the nuanced interactions between multiple factors that affect user susceptibility.
3Measurement precision
If local and global models are integrated, then detection accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary training of local and global models offline using historical data, so that during runtime, the integrated models can quickly generate susceptibility scores without heavy computational burden. The complex model integration work is done in advance, allowing efficient real-time detection while maintaining high accuracy.
Data Source
AI summary
In one embodiment, a method comprises training at least one model based at least in part on interactions between one or more users and electronic messages sent to addresses associated with the one or more users, receiving a first electronic message sent to a first address associated with a first user, analyzing the first electronic message to generate first feature data, determining one or more characteristics of the first user to generate second feature data, inputting, to the at least one model, the first feature data and the second feature data, and receiving, as output of the at least one model, data indicating whether to output, to the first user, a warning regarding the first electronic message.