Explainable Bot Detection Using AI Rule Violation Codebooks
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Solution Overview
Problem
Existing systems struggle to accurately and efficiently detect and respond to computing attacks from automated bots, which can lead to fraud and compromised security in online services.
Innovation Solution
Implement an explainable deep learning bot detection system using generative AI to generate rule violations from telemetry data, which are then used to create rule violation codebooks for neural networks to monitor and classify bot activity, providing explanations for detected violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bot detection methods are used, then the system can detect some malicious activities, but the detection accuracy and efficiency are insufficient against sophisticated automated bots
Solution Approach 1:
The patent replaces traditional rule-based and statistical detection methods with deep learning neural networks that automatically learn complex patterns from telemetry data. The system uses explainable AI models to substitute manual rule creation with automated pattern recognition, achieving both high accuracy and efficiency in bot detection
Solution Approach 2:
The system creates codebooks that store learned patterns and characteristics of bot behavior from training data. These codebooks serve as reusable templates that the neural network references during detection, enabling efficient identification of bot activities without reprocessing entire training datasets
2Reliability
If manual rule creation for bot detection is performed, then detection rules can be customized, but the process requires significant manual effort and time
Solution Approach 1:
The system enables automated generation of detection rules through deep learning models that self-train on telemetry data and automatically create optimized detection codebooks. The explainable AI framework allows the system to autonomously identify patterns and generate detection logic without manual intervention, significantly reducing rule creation time while maintaining high reliability
Solution Approach 2:
The patent implements a training phase where the neural network pre-processes historical telemetry data to learn bot behavior patterns before actual detection begins. This preliminary learning action creates pre-computed codebooks that accelerate real-time detection, eliminating the need for manual rule creation during operational phases
3Measurement precision
If complex deep learning models are used for bot detection, then detection accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent divides the complex detection system into modular components: telemetry data collection modules, neural network processing modules, codebook generation modules, and explanation generation modules. This segmentation allows each component to be optimized independently and simplifies system maintenance while maintaining high detection accuracy through specialized functions in each module
Solution Approach 2:
The system introduces codebooks as intermediary structures between raw telemetry data and detection decisions. These codebooks store learned patterns in a compressed, efficient format that reduces computational complexity during real-time detection while preserving the accuracy benefits of complex deep learning models
Data Source
AI summary
There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.