Personalized Security Testing Simulation for Spear-Phishing Defense

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Solution Overview

Problem

Current systems and methods fail to effectively protect users from the pervasive rise of hyper-targeted spear-phishing attacks, which exploit personalized information to breach security measures, leading to increased susceptibility and risk of data breaches in technology-dependent societies.

Innovation Solution

A computer-implemented method and system for generating personalized security testing simulations that identify users through their profiles and transmit simulated attack communications, analyzing user responses to modify an attack personalization model, thereby enhancing cybersecurity training and awareness by emulating targeted attacks and analyzing vulnerabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional phishing detection systems are implemented, then detection capability is improved, but spear-phishing attacks become more effective and personalized

Engineering Contradiction:

Improvephishing detection capabilityVSAvoidspear-phishing attack effectiveness

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

Instead of only detecting actual attacks, the system sends simulated attack communications to users and measures their responses. This inverts the traditional detection approach by using user behavior responses to simulated attacks as the detection mechanism, thereby improving reliability while countering spear-phishing effectiveness.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The system modifies the attack personalization model based on user responses to simulated attack communications. This feedback loop allows the system to learn from user behavior and improve its detection accuracy over time, addressing the evolving nature of spear-phishing attacks.

Inventive Principle:

Principle #23Feedback

2Object-affected harmful factors

If personalized information is used to target specific victims, then attack effectiveness is improved, but detection risk increases

Engineering Contradiction:

Improveattack effectivenessVSAvoiddetection risk

Core Design Contradiction:

Object-affected harmful factorsVSDifficulty of detecting and measuring

Solution Approach 1:

The system creates simulated attack communications that copy the characteristics and personalization of actual spear-phishing attacks. By analyzing user responses to these simulated copies, the system can detect vulnerability patterns without exposing real targeted attacks, thereby reducing detection risk while maintaining attack effectiveness analysis.

Inventive Principle:

Principle #26Copying

3Reliability

If users are exposed to simulated attack communications, then security awareness is improved, but user convenience is reduced

Engineering Contradiction:

Improvesecurity awarenessVSAvoiduser convenience

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system sends simulated attack communications proactively to users before actual attacks occur. This preliminary action allows users to practice their security responses in a controlled environment, improving security awareness while the automated nature of the system minimizes inconvenience compared to manual training methods.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11477229B2Personalized security testing communication simulations

Publication Date: 2022.10.18 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A method, system, and computer program product for generating personalized security testing simulations is provided. The method identifies a user of a communications system. The user is associated with a user profile. The method generates a simulated attack communication based on the user, the user profile, and an attack personalization model. The simulated attack communication is transmitted to the user via the communications system. The method identifies a user response to the simulated attack communication and modifies the attack personalization model based on the user response.