Reinforcement Learning Agent for Phishing Detection Training
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Solution Overview
Problem
Current phishing detection systems are inaccurately identifying websites, with a lack of diverse training data leading to ineffective detection of phishing webpages, and existing methods like Symantec Online Network for Advanced Response (SONAR) are not robust enough to catch a vast majority of phishing attempts.
Innovation Solution
The use of a reinforcement learning (RL) agent to learn actions that alter webpage content without affecting functionality, allowing the RL agent to evade phishing detection and generate altered content that is classified as benign, thereby improving the training of phishing detectors to increase their accuracy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional phishing detection methods are used, then detection speed is maintained, but detection accuracy is insufficient and cannot identify diverse phishing attempts
Solution Approach 1:
The patent implements dynamic training by continuously generating altered phishing webpages through reinforcement learning and using them to retrain the detection system. This creates a dynamic feedback loop where the detection system adapts to new phishing patterns over time, improving both accuracy and versatility against diverse phishing attempts
Solution Approach 2:
The system performs self-improvement by automatically generating its own training data through the reinforcement learning agent. The altered phishing webpages created by the RL agent serve as self-generated training examples, enabling the system to enhance its own detection capabilities without external intervention
2Measurement precision
If more diverse training data is collected to improve detection accuracy, then detection effectiveness increases, but data collection complexity and time requirements increase
Solution Approach 1:
The system performs preliminary generation of diverse training data by using the reinforcement learning agent to create altered phishing webpages in advance. This pre-generated diverse training data is then used to train the detection system, eliminating the need for time-consuming manual data collection and preparation
Solution Approach 2:
The patent replaces manual data collection and processing with an automated reinforcement learning system. The RL agent automatically generates diverse phishing variations and the system automatically trains on this data, substituting mechanical human efforts with automated computational processes that are faster and more scalable
3Measurement precision
If the phishing detector is trained to be more strict in classification, then detection accuracy improves, but false positive rate increases causing legitimate sites to be misclassified
Solution Approach 1:
The reinforcement learning agent learns to apply localized, targeted alterations to specific features of phishing webpages rather than uniform changes. This enables the generation of diverse training examples that maintain local characteristics of legitimate sites while introducing subtle phishing indicators, helping the detector learn nuanced differentiation without increasing false positives
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for improving phishing webpage content detection. A method can include identifying first webpage content comprises phishing content, determining, using a reinforcement learning (RL) agent, at least one action, generating, based on the determined at least one action and the identified first webpage content, altered first webpage content, identifying that the altered first webpage content is benign, generating, based on the determined at least one action and second webpage content, altered second webpage content, and training, based on the altered second webpage content and a corresponding label of phishing, a phishing detector.