Automated Phishing Detection Rule Evolution via Recursive Scoring
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Solution Overview
Problem
Conventional phishing detection technologies are inefficient in addressing new and constantly changing threats, and they have a significant response time delay due to manual or semi-automatic analysis, leading to a high number of users falling victim to phishing attacks.
Innovation Solution
A system and method for automatically developing and evolving phishing detection rules by applying a set of predefined criteria to quantitative scores of predefined parameters, recursively generating new rules to detect and mitigate phishing content in incoming data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or semi-automatic analysis is used to detect phishing threats, then detection accuracy can be maintained, but response time becomes significantly delayed
Solution Approach 1:
The system automatically evolves detection rules by analyzing phishing patterns and generating updated rules without human intervention. The rule evolution engine autonomously processes phishing indicators, adjusts rule parameters, and creates new detection rules, enabling the system to serve itself in maintaining and improving detection capabilities while reducing response time.
Solution Approach 2:
The system implements a feedback mechanism where detection results and phishing patterns are continuously analyzed to evolve and refine detection rules. The rule evolution engine uses feedback from detected phishing indicators and quantitative scores to automatically adjust and generate improved detection rules, creating a closed-loop system that adapts over time.
2Reliability
If conventional phishing detection technologies are used, then known threats can be detected, but new and constantly changing threats cannot be addressed efficiently
Solution Approach 1:
The detection rules are made dynamic through automatic evolution. Instead of static rules that require manual updates, the system continuously adapts rules based on analyzed phishing patterns and quantitative scores. The rule evolution engine dynamically generates updated rules that reflect current phishing tactics, enabling the system to maintain reliability for known threats while adapting to new threats.
Solution Approach 2:
The system performs preliminary analysis of phishing patterns and evolves detection rules in advance before new threats fully manifest. By continuously analyzing incoming data and pre-evolving rules based on emerging patterns, the system prepares detection capabilities ahead of time, enabling faster response to new threats while maintaining protection against known threats.
3Manufacturing precision
If manual analysis methods are employed, then detection rules can be carefully crafted, but the process becomes time-consuming and inefficient
Solution Approach 1:
The manual mechanical process of rule crafting is replaced with an automated computational system. The rule evolution engine uses algorithms to analyze phishing patterns, calculate quantitative scores, and generate detection rules automatically. This substitution of manual mechanical analysis with automated computational processing maintains rule quality while dramatically improving development efficiency and speed.
Solution Approach 2:
The rule evolution engine acts as an intermediary between raw phishing data and final detection rules. Instead of direct manual analysis, the engine processes data through automated analysis, pattern recognition, and rule generation steps, serving as a mediator that transforms raw information into refined detection rules with high efficiency while preserving quality through systematic processing.
Data Source
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AI summary
System and method for automatically developing phishing detection rules. Based on detected phishing indicia, a quantitative score is computed for each of a plurality of predefined parameters, with each of the parameters relating to at least one of the phishing indicia. A requirement for evolving a phishing detection rule is assessed, and a new phishing detection rule is generated based on selected parameter scores meeting the rule evolution criteria and on corresponding content of the phishing indicia relating to those selected parameter scores. New phishing detection rules are applied recursively to detect phishing indicia, and more new rules can be further evolved in recursive fashion.