Cloud Application Security Using AI Message Clones for BEC Detection
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Solution Overview
Problem
Traditional methods struggle to effectively detect sophisticated business email compromise (BEC) attacks, which are difficult to identify due to their targeted and deceptive nature, often resulting in significant financial losses and reputational harm.
Innovation Solution
A tiered framework utilizing high-speed natural language processing and runtime data augmentation through Generative AI to analyze messages, generating message clones for enhanced detection, including contextual analysis and dynamic confirmation of BEC attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional phishing detection methods are used, then the system is simple and fast, but it cannot detect sophisticated BEC attacks with high accuracy
Solution Approach 1:
The detection system is divided into multiple analysis layers: a first layer for rapid contextual analysis using NLP to classify messages as benign or suspicious, and a second layer for dynamic analysis of suspicious messages using runtime data augmentation and generative models. This segmentation allows the system to maintain simplicity for common cases while deploying complexity only when needed for sophisticated attack detection.
Solution Approach 2:
The system employs runtime data augmentation that dynamically generates variations of incoming messages using generative AI models, allowing the detection accuracy to adapt to new attack patterns in real-time. This dynamic approach enables the system to maintain high accuracy against evolving BEC attacks without requiring manual retraining of detection models.
2Measurement precision
If runtime data augmentation with generative models is applied, then detection accuracy for new attack patterns improves, but processing time and computational resources increase
Solution Approach 1:
The system segments the processing workflow into a fast first layer for contextual analysis that handles the majority of messages quickly, and a slower second layer for dynamic analysis that is only activated for suspicious messages. This ensures that runtime data augmentation and generative model processing are performed only when necessary, minimizing overall processing time while maintaining high accuracy.
Solution Approach 2:
The system applies runtime data augmentation and generative model analysis only to a subset of messages identified as suspicious by the first layer, rather than processing all incoming messages through the full dynamic analysis pipeline. This partial application of computational resources maintains high detection accuracy for targeted attacks while reducing overall processing time and computational overhead.
3Measurement precision
If the system analyzes all message aspects in detail, then detection accuracy improves, but the system becomes slower and less efficient
Solution Approach 1:
The analysis process is segmented into two distinct layers: the first layer performs rapid contextual analysis on all messages to identify suspicious ones, while the second layer performs detailed dynamic analysis only on the subset of suspicious messages. This segmentation enables the system to maintain high processing throughput for the majority of benign messages while dedicating resources to thorough analysis only when needed.
Solution Approach 2:
The system applies comprehensive aspect analysis through runtime data augmentation and generative models only to suspicious messages identified by the first layer, rather than analyzing all message aspects for every incoming message. This partial analysis approach maintains high detection accuracy for sophisticated attacks while preserving overall processing efficiency and throughput.
Data Source
AI summary
Provided herein are systems and methods for detecting a phishing attack. The method comprises: processing an original message to determine whether the original message is suspicious or benign, upon determining the original message is suspicious, generating multiple copies of the original message for detecting a phishing attack, where the multiple copies are varied from the original message in least one of tones, formats, and writing styles, and the multiple copies are generated to be similar to a training dataset that is utilized to train a phishing attack detection engine; and processing the multiple copies and the original message by the phishing attack detection engine to determine whether the original message is malicious or benign. One or more copies from the multiple copies that are not identified as malicious are utilized to further train the phishing attack detection engine automatically.